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García-Ruiz, Pablo, Francisco J. Romero-Ramirez, Rafael Muñoz-Salinas, Manuel J. Marín-Jiménez, and Rafael Medina-Carnicer. "Fiducial Objects: Custom Design and Evaluation." Sensors 23, no. 24 (2023): 9649. http://dx.doi.org/10.3390/s23249649.
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Camera pose estimation is vital in fields like robotics, medical imaging, and augmented reality. Fiducial markers, specifically ArUco and Apriltag, are preferred for their efficiency. However, their accuracy and viewing angle are limited when used as single markers. Custom fiducial objects have been developed to address these limitations by attaching markers to 3D objects, enhancing visibility from multiple viewpoints and improving precision. Existing methods mainly use square markers on non-square object faces, leading to inefficient space use. This paper introduces a novel approach for creating fiducial objects with custom-shaped markers that optimize face coverage, enhancing space utilization and marker detectability at greater distances. Furthermore, we present a technique for the precise configuration estimation of these objects using multiviewpoint images. We provide the research community with our code, tutorials, and an application to facilitate the building and calibration of these objects. Our empirical analysis assesses the effectiveness of various fiducial objects for pose estimation across different conditions, such as noise levels, blur, and scale variations. The results suggest that our customized markers significantly outperform traditional square markers, marking a positive advancement in fiducial marker-based pose estimation methods.
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Mtasher, Ashwaq Katham, and Esraa Hassan Jawad Al-wakel. "Custom Object Detection Using Transfer Learning with Pretrained Models for Improved Detection Techniques." Journal La Multiapp 5, no. 1 (2024): 10–18. http://dx.doi.org/10.37899/journallamultiapp.v5i1.843.
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Custom object detection plays a vital role in computer vision applications. However, developing an accurate and efficient custom object detector requires a substantial amount of labeled training data and significant computational resources. In this research, we propose a custom object detection framework that leverages transfer learning with pre-trained models to improve detection tech-niques.The framework first utilizes a pre-trained deep learning model, such as ResNet or VGGNet, as a feature extractor. The pre-trained model is trained on a large-scale dataset, enabling it to learn high-level features from various objects. By reusing the pre-trained model's convolutional layers, we effectively capture generic features that can be transferred to the custom object detection task.Experimental evaluations on benchmark datasets demonstrate the effectiveness of our ap-proach. The custom object detector achieved superior detection performance compared to tradi-tional methods, especially when the target objects have limited training data. Additionally, our framework significantly reduces the amount of training time and computational resources required, as it leverages pre-trained models as a starting point.
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Kumar, Aayush, Amit Kumar, Avanish Chandra, and Indira Adak. "Custom Object Detection and Analysis in Real Time: YOLOv4." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3982–90. http://dx.doi.org/10.22214/ijraset.2022.43303.
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Abstract: Object recognition is one of the most basic and complex problems in computer vision, which seeks to locate object instances from the enormous categories of already defined and readily available natural images. The object detection method aims to recognize all the objects or entities in the given picture and determine the categories and position information to achieve machine vision understanding. Several tactics have been put forward to solve this problem, which is more or less inspired by the principles based on Open Source Computer Vision Library (OpenCV) and Deep Learning. Some are relatively good, while others fail to detect objects with random geometric transformations. This paper proposes demonstrating the " HAWKEYE " application, a small initiative to build an application working on the principle of EEE i.e. (Explore→Experience→Evolve). Keywords: Convolution Neural Network, Object detection, Image classification, Deep learning, Open CV, Yolov4.
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Achirei, Stefan-Daniel, Razvan Mocanu, Alexandru-Tudor Popovici, and Constantin-Catalin Dosoftei. "Model-Predictive Control for Omnidirectional Mobile Robots in Logistic Environments Based on Object Detection Using CNNs." Sensors 23, no. 11 (2023): 4992. http://dx.doi.org/10.3390/s23114992.
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Object detection is an essential component of autonomous mobile robotic systems, enabling robots to understand and interact with the environment. Object detection and recognition have made significant progress using convolutional neural networks (CNNs). Widely used in autonomous mobile robot applications, CNNs can quickly identify complicated image patterns, such as objects in a logistic environment. Integration of environment perception algorithms and motion control algorithms is a topic subjected to significant research. On the one hand, this paper presents an object detector to better understand the robot environment and the newly acquired dataset. The model was optimized to run on the mobile platform already on the robot. On the other hand, the paper introduces a model-based predictive controller to guide an omnidirectional robot to a particular position in a logistic environment based on an object map obtained from a custom-trained CNN detector and LIDAR data. Object detection contributes to a safe, optimal, and efficient path for the omnidirectional mobile robot. In a practical scenario, we deploy a custom-trained and optimized CNN model to detect specific objects in the warehouse environment. Then we evaluate, through simulation, a predictive control approach based on the detected objects using CNNs. Results are obtained in object detection using a custom-trained CNN with an in-house acquired data set on a mobile platform and in the optimal control for the omnidirectional mobile robot.
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Haokip, M. Hemminlal, and Ngamkholen Haokip. "Re-interpreting the traditional Semiotic Culture of ChinKuki-Mizo." International Journal of Humanities and Social Science Invention 14, no. 3 (2025): 91–96. https://doi.org/10.35629/7722-14039196.
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The Chin-Kuki-Mizos have various symbolisms representing ideas or characters, attributing them to natural objects or facts in their culture. The Chin-Kuki-Mizos are the ethnic group that has traced its origin to a mythological cave and are currently settled in different countries: Bangladesh, Myanmar and India. The rich customs and culture regarding signs, meanings and concepts are bound to their art and crafts. The Chin-KukiMizos have specific customs, symbols, and signs to convey good and bad messages. All the interpretations, beliefs and superstitions are related to their social, economic, cultural, custom and political perspectives. The paper scientifically examines and interprets the traditional symbolism, meanings and concepts based on their socio-economy and custom culture.
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Said, Yahia, Mohamed Atri, Marwan Ali Albahar, Ahmed Ben Atitallah, and Yazan Ahmad Alsariera. "Indoor Signs Detection for Visually Impaired People: Navigation Assistance Based on a Lightweight Anchor-Free Object Detector." International Journal of Environmental Research and Public Health 20, no. 6 (2023): 5011. http://dx.doi.org/10.3390/ijerph20065011.
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Facilitating the navigation of visually impaired people in indoor environments requires detecting indicating signs and informing them. In this paper, we proposed an indoor sign detection based on a lightweight anchor-free object detection model called FAM-centerNet. The baseline model of this work is the centerNet, which is an anchor-free object detection model with high performance and low computation complexity. A Foreground Attention Module (FAM) was introduced to extract target objects in real scenes with complex backgrounds. This module segments the foreground to extract relevant features of the target object using midground proposal and boxes-induced segmentation. In addition, the foreground module provides scale information to improve the regression performance. Extensive experiments on two datasets prove the efficiency of the proposed model for detecting general objects and custom indoor signs. The Pascal VOC dataset was used to test the performance of the proposed model for detecting general objects, and a custom dataset was used for evaluating the performance in detecting indoor signs. The reported results have proved the efficiency of the proposed FAM in enhancing the performance of the baseline model.
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Uličná, Lenka. "Disposed or Concealed: The Intriguing Case of Genizah Shoes." Ars Judaica 19, no. 1 (2023): 93–108. http://dx.doi.org/10.3828/arsjudaica.2023.19.8.
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Central European genizot, protected and closed spaces, usually in a synagogue attic, were for centuries used by local communities as a storage area for worn-out ritual objects, especially textual relics. However, beside the expected objects requiring genizah according to halakhah, genizot also contain objects that have no obvious connection to religion. Most remarkable is the abundance of shoes of various types and materials. These may have belonged to the deceased and as such, according to Jewish custom, should not be worn by anyone else. The practice of setting aside shoes, however, may have been influenced by non-Jewish customs.
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Jayakumar, Dontabhaktuni, and Samineni Peddakrishna. "Performance Evaluation of YOLOv5-based Custom Object Detection Model for Campus-Specific Scenario." International Journal of Experimental Research and Review 38 (April 30, 2024): 46–60. http://dx.doi.org/10.52756/ijerr.2024.v38.005.
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This study evaluates the performance of a custom object detection model based on the YOLOv5 architecture, specifically tailored for autonomous electric vehicles. The model undergoes pre-processing using the Roboflow computer vision platform, which offers a wide range of tools for data pre-processing and model training. The experiments were conducted on a diverse dataset comprising various objects encountered in campus-specific driving scenarios, such as pedestrians, vehicles, buildings, and obstacles. The performance of the custom object detection model is assessed using standard metrics, including precision, recall, mean average precision (mAP), and intersection-over-union (IoU) at different thresholds. The training process was conducted in a controlled environment, resulting in a Precision of 0.851, a Recall of 0.831, and a mAP of 0.843. These metrics were analyzed to evaluate the YOLOv5-based custom object detection model's ability to detect and categorize objects accurately, its precision in predicting bounding boxes, and its capability to handle various object categories. We also examined the effects of different hyperparameters and data augmentation techniques on the model's performance, including variations in learning rate, batch size, and optimizer algorithms to determine their impact on accuracy and convergence. This analysis provided valuable insights into the model's strengths and weaknesses, highlighting areas for improvement and optimization. These findings are instrumental in developing and deploying advanced object detection systems to enhance the safety and reliability of autonomous electric vehicles.
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Sankar, Krishnakumar, Shantanu Patil, and Sridhar Krishnamurthy. "Analysis of grip and pinch strength using inverse dynamics simulation technique." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine 232, no. 11 (2018): 1063–70. http://dx.doi.org/10.1177/0954411918798400.
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Grip strength is the function of musculotendinous action across the finger joints and varies while dealing objects of varying size and shape. Increasing object size requires greater effort by the fingers to grip. The main objective of this study is to analyse the variation in grip and pinch strength exerted on objects of various sizes and shapes in a short span of time. OpenSim 3.3 is open-source musculoskeletal modelling software used for performing simulations in a dynamic environment. The generic wrist model in OpenSim has movements on index finger and thumb only, for study purpose. Objects of various sizes were designed and imported in OpenSim. This study determines the joint moments exerted by the fingers during grip activities. Original model was modified and custom joints were placed in the proximal and distal phalanx joints of the fingers. This allowed the finger segments to undergo translational and rotational movements. Coordinates of the custom joints were adjusted to provide constraints in the joint movement to hold the objects in position. For grip strength, objects of various sizes and shapes were imported to OpenSim. Simulation was carried out for gripping the objects for a specified time period. The force generated by the synergistic movements of finger segments was compared among grip and pinch of different objects. This method is used to determine the grip and pinch strength by handling objects of different shapes and sizes under the influence of time.
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Reddy, Shiva Shankar, Venkata Rama Maheswara Rao, Priyadarshini Voosala, and Silpa Nrusimhadri. "You only look once model-based object identification in computer vision." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 827. http://dx.doi.org/10.11591/ijai.v13.i1.pp827-838.
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<span lang="EN-US">You only look once version 4 (YOLOv4) is a deep-learning object detection algorithm. It is used to decrease parameters and simplify network structures, making it suited for mobile and embedded device development. The YOLO detector can foresee an object's Class, bounding box, and probability of that Object's Class being found inside that bounding box. A probability value for each bounding box represents the likelihood of a given item class in that bounding box. Global features, channel attention, and special attention are also applied to extract more compelling information. Finally, the model combines the auxiliary and backbone networks to create the YOLOv4's entire network topology. Using custom functions developed upon YOLOv4, we get the count of the objects and a crop around the objects detected with a confidence score that specifies the probability of the thing seen being the same Class as predicted by YOLOv4. A confidence threshold is implemented to eliminate the detections with low confidence. </span>
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Shankar, Reddy Shiva, Rao Venkata Rama Maheswara, Priyadarshini Voosala, and Silpa Nrusimhadri. "You only look once model-based object identification in computer vision." IAES International Journal of Artificial Intelligence (IJ-AI) 13, no. 1 (2024): 827–38. https://doi.org/10.11591/ijai.v13.i1.pp827-838.
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You only look once version 4 (YOLOv4) is a deep-learning object detection algorithm. It is used to decrease parameters and simplify network structures, making it suited for mobile and embedded device development. The YOLO detector can foresee an object's Class, bounding box, and probability of that Object's Class being found inside that bounding box. A probability value for each bounding box represents the likelihood of a given item class in that bounding box. Global features, channel attention, and special attention are also applied to extract more compelling information. Finally, the model combines the auxiliary and backbone networks to create the YOLOv4's entire network topology. Using custom functions developed upon YOLOv4, we get the count of the objects and a crop around the objects detected with a confidence score that specifies the probability of the thing seen being the same Class as predicted by YOLOv4. A confidence threshold is implemented to eliminate the detections with low confidence.
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Firman, Firman, Helfy Susilawati, Arief Suryadi Setyawan, and Mokh Mirza Etnisa Haqiqi. "Custom LiDAR Dataset for 3D Object Recognition in Restricted Spaces Using Voxel-RCNN." MDP Student Conference 4, no. 1 (2025): 425–30. https://doi.org/10.35957/mdp-sc.v4i1.11209.
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Autonomous vehicles play a crucial role in logistics, agriculture, and warehousing, requiring precise object detection and recognition for safe navigation in confined spaces. Traditional 2D sensor-based methods and simple LiDAR applications often struggle with depth perception and classification accuracy, limiting real-time decision-making. This study addresses these challenges by developing a custom LiDAR-based dataset for object recognition within the Voxel-RCNN framework, focusing on six object categories to enhance recognition accuracy. The Voxel-RCNN model was trained on this custom dataset without architectural modifications, assessing its generalization to non-standard data and performance in constrained environments. The training process demonstrated stable convergence, with loss decreasing from 6.09 to 2.37 after 600 epochs. Quantitative evaluations using BEV and 3D Average Precision (AP) revealed strong performance in detecting structured objects like cars (68.14% BEV AP, 55.83% 3D AP in Easy cases) but significant challenges with occluded and irregularly shaped objects such as trees and cyclists. Despite these challenges, the study highlights the potential of Voxel-RCNN for 3D object recognition in autonomous navigation. Future improvements include dataset augmentation, multi-scale feature fusion, and advanced voxelization techniques to enhance recognition performance. These findings contribute to the advancement of LiDAR-based perception systems, supporting safer and more intelligent autonomous vehicle operations.
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Ravi, Niranjan, Sami Naqvi, and Mohamed El-Sharkawy. "BIoU: An Improved Bounding Box Regression for Object Detection." Journal of Low Power Electronics and Applications 12, no. 4 (2022): 51. http://dx.doi.org/10.3390/jlpea12040051.
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Object detection is a predominant challenge in computer vision and image processing to detect instances of objects of various classes within an image or video. Recently, a new domain of vehicular platforms, e-scooters, has been widely used across domestic and urban environments. The driving behavior of e-scooter users significantly differs from other vehicles on the road, and their interactions with pedestrians are also increasing. To ensure pedestrian safety and develop an efficient traffic monitoring system, a reliable object detection system for e-scooters is required. However, existing object detectors based on IoU loss functions suffer various drawbacks when dealing with densely packed objects or inaccurate predictions. To address this problem, a new loss function, balanced-IoU (BIoU), is proposed in this article. This loss function considers the parameterized distance between the centers and the minimum and maximum edges of the bounding boxes to address the localization problem. With the help of synthetic data, a simulation experiment was carried out to analyze the bounding box regression of various losses. Extensive experiments have been carried out on a two-stage object detector, MASK_RCNN, and single-stage object detectors such as YOLOv5n6, YOLOv5x on Microsoft Common Objects in Context, SKU110k, and our custom e-scooter dataset. The proposed loss function demonstrated an increment of 3.70% at APS on the COCO dataset, 6.20% at AP55 on SKU110k, and 9.03% at AP80 of the custom e-scooter dataset.
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Heredia, Jorge David Figueroa, Hamdi Sahloul, and Jun Ota. "Teaching Mobile Robots Using Custom-Made Tools by a Semi-Direct Method." Journal of Robotics and Mechatronics 28, no. 2 (2016): 242–54. http://dx.doi.org/10.20965/jrm.2016.p0242.
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[abstFig src='/00280002/15.jpg' width=""300"" text='Teach grasping point by custom-made tool' ]We propose a method for conveying human knowledge to home and office assistance robots by teaching them how to perform the process of grasping objects with a custom-made tool. Specifically, we propose a semi-direct teaching method that respects the limitations of the hardware on the robot while utilizing human experience for intuitive teaching. We specify the information necessary for grasping objects through the generation of teaching data, which include the grasping force, relative position, and orientation. To respect the hardware limitations and at the same time allow inexperienced users to perform the teaching process easily, we used a teaching tool that possesses the same mechanism as the end effector of the robot. To simplify the teaching, we developed a sensing system that would reduce the teaching time with accurate measurements. Subsequently, the robot would use the teaching data to grasp the object. Experiments conducted using volunteers demonstrated the validity of the proposed method, wherein the teaching data for three different tasks were generated in less than 30 s each and accurate measurements were obtained for both the grasping position and force for grasping the objects.
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Cmiel, Vratislav, Larisa Chmelikova, Inna Zumberg, and Martin Kralik. "A Novel Gesture-Based Control System for Fluorescence Volumetric Data in Virtual Reality." Sensors 21, no. 24 (2021): 8329. http://dx.doi.org/10.3390/s21248329.
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With the development of light microscopy, it is becoming increasingly easy to obtain detailed multicolor fluorescence volumetric data. The need for their appropriate visualization has become an integral part of fluorescence imaging. Virtual reality (VR) technology provides a new way of visualizing multidimensional image data or models so that the entire 3D structure can be intuitively observed, together with different object features or details on or within the object. With the need for imaging advanced volumetric data, demands for the control of virtual object properties are increasing; this happens especially for multicolor objects obtained by fluorescent microscopy. Existing solutions with universal VR controllers or software-based controllers with the need to define sufficient space for the user to manipulate data in VR are not usable in many practical applications. Therefore, we developed a custom gesture-based VR control system with a custom controller connected to the FluoRender visualization environment. A multitouch sensor disk was used for this purpose. Our control system may be a good choice for easier and more comfortable manipulation of virtual objects and their properties, especially using confocal microscopy, which is the most widely used technique for acquiring volumetric fluorescence data so far.
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Konakalla, Kiran. "Building an End-to-End Hiring Process in Salesforce: Automating Recruitment with Custom Objects, Approval Processes, and Lightning Components." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem8820.
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The process of hiring employees involves numerous steps that can be complex and time-consuming for HR teams. In this paper, we explore how Salesforce can be customized to streamline the hiring process end to end, from the moment a candidate submits an application to the final approval and onboarding. Using custom objects, automation, and integrations within Salesforce, we will develop an efficient system to track interview stages, manage interviewer feedback, and automate approval processes, resulting in a smoother, more organized hiring process. The paper includes key custom fields, workflow automation, and Apex code examples to showcase the technical implementation. Keywords Salesforce, Hiring Process, Automation, Interview Stages, Custom Objects, Workflow, Process Builder, Apex, HR Solutions, Recruiter
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Simovych, Oksana. "From Ladder and Thread to Heaven: The Symbolic Meaning of the Path in a Fragment of the Linguistic World Image." Linguistics, no. 2 (44) (2021): 38–52. http://dx.doi.org/10.12958/2227-2631-2021-2-44-38-52.
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This article «From Ladder and Thread to Heaven: The Symbolic Meaning of the Path in a Fragment of the Linguistic World Image» explores the problem of the analysis of folk customs. These customs could be verbalized both in folk texts and in dialects. The specifics of this study lie in the linguistic analysis of the symbols which are usually interpreted as folk customs and folk objects. However, the symbolism of the objects in national customs causes the development of a symbolic meaning of the respective word that defines these objects. In this way, many symbols in folk customs become verbal, and the context of the custom creates a foundation for the development of the symbolic meaning. The verbal symbols analyzed are a «thread», a «ball of twine», a «ladder», a «bridge» and a «cross». In the national Ukrainian linguistic space, these words have the general semantics of the ‘connection between worlds’. It is stressed that the symbolic meaning of the (celestial) ladder has been discovered in the biblical context. This is also relevant for the clarification of the subject of continuity in the development of the symbolic meanings, which are also documented in the Ukrainian context. A concrete situation in linguistics and custom creates conditions under which arise symbolic co-meanings that develop in the framework of the same main symbolic archetypical meaning. All analyzed symbols belong to the archetypical ones. That is why they have been also discovered with the same semantics in other languages. This is the reason why the analysis of such symbols requires not only facts documented in the dictionaries and texts in Ukrainian, but also information about the respective symbol in other linguistic cultures. It is also pointed out that the thread is analyzed as an apotropaic symbol. This word has also been documented linguistically as a symbol of the demarcation line between one’s own world and the world of «others».
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Aujeszky, Tamas, Georgios Korres, Mohamad Eid, and Farshad Khorrami. "Estimating Weight of Unknown Objects Using Active Thermography." Robotics 8, no. 4 (2019): 92. http://dx.doi.org/10.3390/robotics8040092.
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Successful manipulation of unknown objects requires an understanding of their physical properties. Infrared thermography has the potential to provide real-time, contactless material characterization for unknown objects. In this paper, we propose an approach that utilizes active thermography and custom multi-channel neural networks to perform classification between samples and regression towards the density property. With the help of an off-the-shelf technology to estimate the volume of the object, the proposed approach is capable of estimating the weight of the unknown object. We show the efficacy of the infrared thermography approach to a set of ten commonly used materials to achieve a 99.1% R 2 -fit for predicted versus actual density values. The system can be used with tele-operated or autonomous robots to optimize grasping techniques for unknown objects without touching them.
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Widyadara, Made Ayu Dusea, and Marga Asta Jaya Mulya. "Comparing YOLOv5 and YOLOv8 Performance in Vehicle License Plate Detection." International Journal of Research and Review 12, no. 2 (2025): 8–17. https://doi.org/10.52403/ijrr.20250202.
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The number of mobile vehicles on the roads in Indonesia is increasing every year. Therefore, it is essential to verify the identities of these vehicles for a variety of reasons, including locating stolen vehicles, enforcing traffic laws, managing car parks, and collecting tolls. Nevertheless, inspecting these vast numbers of vehicles manually is a challenging task. Motor vehicle number plate detection and recognition play a crucial role in intelligent transport systems. Generally, the detection and recognition of number plates on motor vehicles entail three main stages. Machine learning-based object detection, which encompasses a range of algorithms that can automatically identify and locate objects in images or videos, is the first stage. These models leverage multiple layers of processing units to extract intricate features from input data, thereby enhancing overall efficiency for object detection purposes. The YOLO algorithm is a popular object detection algorithm that can detect objects from images or videos in real-time using custom dataset. In this study, we directly compared YOLOv5 and YOLOv8 models which underwent equal training epochs, achieved stability, and utilized hyperparameters with an image size 640, 100 epochs, val 200, and batch 16. The YOLOv8 gets the best performance with almost 97.5% mAP and 69.4% mAP50-95. Keywords: Plate Number, YOLOv5, YOLOv8, Object Detection, Custom Dataset
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Rao Ulisi, Uma Maheswara. "Overview of Cloud-Based Custom Objects in ERP Cloud Implementation." International Journal of Scientific Research and Engineering Trends 11, no. 2 (2025): 1426–29. https://doi.org/10.61137/ijsret.vol.11.issue2.236.
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Kumail, Saifuddin Saif. "FI Custom Code block Extension in SAP S/4 HANA Finance." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 9, no. 6 (2021): 1–6. https://doi.org/10.5281/zenodo.14593179.
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To accommodate additional custom fields or reporting requirements, it is necessary to extend the Universal Journal with custom code blocks, ensuring all business-specific data is captured and available for analysis. There are two ways to extend code blocks. The Classic method is using the OXK3 transaction and the CI_COBL structure. The other method is using the Custom FieldsFiori app. Furthermore, you can use custom fields along with predefined business scenarios so that all involved business objects are extended, and values are passed along automatically.
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Datti, Jyothsna, and Ramesh Chandra Gollapudi. "Three-Dimensional Object Detection in Point Clouds with Multi-Stage Proposal Refinement Network." Journal of Robotics and Control (JRC) 6, no. 2 (2025): 745–56. https://doi.org/10.18196/jrc.v6i2.25602.
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Three-dimensional object detection in point clouds serves a vital role in autonomous driving and robotics. Point Clouds provide a vivid representation of 3D data that enables reliable object detection by acquiring the spatial distribution of points in a scene, facilitating the localization and identification of the objects within three-dimensional space. Precise localization of the objects remains challenging, particularly for moderately visible objects which attributes to inconsistent quality proposals. To tackle this, this paper presents a multi-stage proposal refinement network to generate the qualitative predictions. The research contribution is, first to improve the quality of proposals in partially visible objects, the model is integrated with 3D Resnet backbone through the refinement module at various stages. Second, to improve the quality of predictions, a confidence-weighted box voting mechanism is incorporated ensuring the precise bounding box detections. Experimentation analysis was carried out on the KITTI, NuScenes and the custom LIDAR datasets. Notably, the proposed method achieves an average precision of 82.45% for Car class, 44.94% for Pedestrian class and 66.12% for Cyclist class on the moderate category of KITTI dataset, but in the hard category with high occlusion need to be improved. On Nuscenes dataset, the model achieved mAP of 66.2%. In custom dataset, 2739 training frames, 342 frames for validation, and 343 frames for testing were taken which achieved an average precision of 82.40% for Car, 44.10% for pedestrian and 67.90% for Cyclist. The results indicate that multi-stage refinement network enhances to perform the object detection precisely, which is critical to localize and detect the target in autonomous driving and robotics.
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King, Oliver R. T., Leigh N. Fletcher, Jake Harkett, Michael T. Roman, and Henrik Melin. "Custom JWST NIRSpec/IFU and MIRI/MRS Data Reduction Pipelines for Solar System Targets." Research Notes of the AAS 7, no. 10 (2023): 223. http://dx.doi.org/10.3847/2515-5172/ad045f.
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Abstract We present custom JWST data reduction pipelines for JWST NIRSpec/IFU and MIRI/MRS observations of solar system objects. The pipelines simplify the process of reducing the JWST observations, and include custom steps to significantly improve the data quality. Our custom processing routines include a “desaturation” routine to reduce the effect of saturation while still maintaining high signal-to-noise ratio, and custom flat field correction code to remove the significant artifacts found in MIRI/MRS observations. The pipelines also automatically generate a series of quick look plots and animations to simplify exploration of a dataset. These custom JWST pipelines can be downloaded from https://github.com/JWSTGiantPlanets/pipelines.
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Daramouskas, Ioannis, Dimitrios Meimetis, Niki Patrinopoulou, Vaios Lappas, Vassilios Kostopoulos, and Vaggelis Kapoulas. "Camera-Based Local and Global Target Detection, Tracking, and Localization Techniques for UAVs." Machines 11, no. 2 (2023): 315. http://dx.doi.org/10.3390/machines11020315.
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Multiple-object detection, localization, and tracking are desirable in many areas and applications, as the field of deep learning has developed and has drawn the attention of academics in computer vision, having a plethora of networks now achieving excellent accuracy in detecting multiple objects in an image. Tracking and localizing objects still remain difficult processes which require significant effort. This work describes an optical camera-based target detection, tracking, and localization solution for Unmanned Aerial Vehicles (UAVs). Based on the well-known network YOLOv4, a custom object detection model was developed and its performance was compared to YOLOv4-Tiny, YOLOv4-608, and YOLOv7-Tiny. The target tracking algorithm we use is based on Deep SORT, providing cutting-edge tracking. The proposed localization approach can accurately determine the position of ground targets identified by the custom object detection model. Moreover, an implementation of a global tracker using localization information from up to four UAV cameras at a time. Finally, a guiding approach is described, which is responsible for providing real-time movement commands for the UAV to follow and cover a designated target. The complete system was evaluated in Gazebo with up to four UAVs utilizing Software-In-The-Loop (SITL) simulation.
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Wu, Jie. "Enhancing Object Sorting Under Low-Light Conditions with CLAHE, Gaussian Blur, ROI, and Custom PID on a Raspberry Pi Robotic Arm." Applied and Computational Engineering 96, no. 1 (2024): 93–98. http://dx.doi.org/10.54254/2755-2721/96/20241240.
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Abstract. This paper addresses the significant challenge faced by robotic vision systems in detecting and sorting objects accurately under varying lighting conditions. Such variations in light can lead to decreased detection accuracy and inefficiencies in automated sorting processes. The paper employs a combination of literature review and experimental validation to investigate the effectiveness of advanced image processing techniques and control algorithms. Specifically, it explores the application of CLAHE adaptive compensation, Gaussian Blur, custom ROI, and PID controllers within a visual object sorting system to improve its robustness under diverse lighting conditions. The use of CLAHE and Gaussian Blur effectively compensates for uneven lighting, while custom ROI and PID controllers further optimize the system's response to fluctuating conditions.
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Erdenebat, Munkh-Uchral, Tuvshinjargal Amgalan, Anar Khuderchuluun, et al. "Comprehensive High-Quality Three-Dimensional Display System Based on a Simplified Light-Field Image Acquisition Method and a Full-Connected Deep Neural Network." Sensors 23, no. 14 (2023): 6245. http://dx.doi.org/10.3390/s23146245.
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We propose a high-quality, three-dimensional display system based on a simplified light field image acquisition method, and a custom-trained full-connected deep neural network is proposed. The ultimate goal of the proposed system is to acquire and reconstruct the light field images with possibly the most elevated quality from the real-world objects in a general environment. A simplified light field image acquisition method acquires the three-dimensional information of natural objects in a simple way, with high-resolution/high-quality like multicamera-based methods. We trained a full-connected deep neural network model to output desired viewpoints of the object with the same quality. The custom-trained instant neural graphics primitives model with hash encoding output the overall desired viewpoints of the object within the acquired viewing angle in the same quality, based on the input perspectives, according to the pixel density of a display device and lens array specifications within the significantly short processing time. Finally, the elemental image array was rendered through the pixel re-arrangement from the entire viewpoints to visualize the entire field-of-view and re-constructed as a high-quality three-dimensional visualization on the integral imaging display. The system was implemented successfully, and the displayed visualizations and corresponding evaluated results confirmed that the proposed system offers a simple and effective way to acquire light field images from real objects with high-resolution and present high-quality three-dimensional visualization on the integral imaging display system.
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Liu, Zhiguo, Enzheng Zhang, Qian Ding, Weijie Liao, and Zixiang Wu. "An Improved Method for Enhancing the Accuracy and Speed of Dynamic Object Detection Based on YOLOv8s." Sensors 25, no. 1 (2024): 85. https://doi.org/10.3390/s25010085.
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Accurate detection and tracking of dynamic objects are critical for enabling skill demonstration and effective skill generalization in robotic skill learning and application scenarios. To further improve the detection accuracy and tracking speed of the YOLOv8s model in dynamic object tracking tasks, this paper proposes a method to enhance both detection precision and speed based on YOLOv8s architecture. Specifically, a Focused Linear Attention mechanism is introduced into the YOLOv8s backbone network to enhance dynamic object detection accuracy, while the Ghost module is incorporated into the neck network to improve the model’s tracking speed for dynamic objects. By mapping the motion of dynamic objects across frames, the proposed method achieves accurate trajectory tracking. This paper provides a detailed explanation of the improvements made to YOLOv8s for enhancing detection accuracy and speed in dynamic object detection tasks. Comparative experiments on the MS-COCO dataset and the custom dataset demonstrate that the proposed method has a clear advantage in terms of detection accuracy and processing speed. The dynamic object detection experiments further validate the effectiveness of the proposed method for detecting and tracking objects at different speeds. The proposed method offers a valuable reference for the field of dynamic object detection, providing actionable insights for applications such as robotic skill learning, generalization, and artificial intelligence-driven robotics.
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Pandey, Shweta. "Leveraging Workday for Effective Covid-19 Vaccination Tracking: Integrating Custom Objects and Security Features in Human Capital Management Systems." International Journal of Business Quantitative Economics and Applied Management Research 7, no. 1 (2021): 56–63. https://doi.org/10.5281/zenodo.14176000.
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Employee workforce is an asset to the organization, forming the foundation of every successful business. Effective Human Resource (HR) management is crucial in ensuring employees feel safe, valued, and properly supported. The HR department is responsible for maintaining employee safety, health, and satisfaction. In the wake of the COVID-19 pandemic, as companies plan to bring their workforce back to the office and covid vaccination tracking under state and federal mandates, it is critical to track employees' COVID-19 vaccination status in the Human Capital Management (HCM) system to ensure workplace safety. This article explores the use of custom objects, fields, domains, and custom object validation for tracking employee COVID-19 vaccination status and discusses specific tools and customizable solutions offered by Workday to facilitate this process.
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Yusardi, Rahmanditto Yusardi,, and M. Nasrul Kamal. "Perancangan Magazine Motor Custom di Kota Padang." DEKAVE : Jurnal Desain Komunikasi Visual 11, no. 1 (2021): 62. http://dx.doi.org/10.24036/dekave.v11i1.112334.
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Custom motorcycle was born to adapt to a builder and owner's will on character, shape, idea, and imagination. The custom culture is a primary part of a custom motorcycle. Printed media such as magazine plays an active role in developing custom culture in Padang. Magazine becomes a source of information capturing history on years and objects which are used as content. This magazine contains specifications and descriptions from some custom motorcycle's owners. With macro and micro photography techniques, the information is expected to be easily understood. The purpose of this design is to provide information regarding custom motorcycles with people interested in the custom motorcycle scheme as the target audience. The method of this design is Four-D method: define, design, development, disseminate. The method of analysis is 5W+1H (what, where, who, when, why, and how). The main medium in this design is magazine, simply arranged so it is easy to understand, and supported by media such as t-shirt, headband, tote bag, keychain, sketchbook, sticker, and Instagram feed so the information of this design is right on target.Keywords: Custom Motorcyle, Magazine.
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Jia, Yin, Balakrishnan Ramalingam, Rajesh Elara Mohan, Zhenyuan Yang, Zimou Zeng, and Prabakaran Veerajagadheswar. "Deep-Learning-Based Context-Aware Multi-Level Information Fusion Systems for Indoor Mobile Robots Safe Navigation." Sensors 23, no. 4 (2023): 2337. http://dx.doi.org/10.3390/s23042337.
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Hazardous object detection (escalators, stairs, glass doors, etc.) and avoidance are critical functional safety modules for autonomous mobile cleaning robots. Conventional object detectors have less accuracy for detecting low-feature hazardous objects and have miss detection, and the false classification ratio is high when the object is under occlusion. Miss detection or false classification of hazardous objects poses an operational safety issue for mobile robots. This work presents a deep-learning-based context-aware multi-level information fusion framework for autonomous mobile cleaning robots to detect and avoid hazardous objects with a higher confidence level, even if the object is under occlusion. First, the image-level-contextual-encoding module was proposed and incorporated with the Faster RCNN ResNet 50 object detector model to improve the low-featured and occluded hazardous object detection in an indoor environment. Further, a safe-distance-estimation function was proposed to avoid hazardous objects. It computes the distance of the hazardous object from the robot’s position and steers the robot into a safer zone using detection results and object depth data. The proposed framework was trained with a custom image dataset using fine-tuning techniques and tested in real-time with an in-house-developed mobile cleaning robot, BELUGA. The experimental results show that the proposed algorithm detected the low-featured and occluded hazardous object with a higher confidence level than the conventional object detector and scored an average detection accuracy of 88.71%.
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Baran, Bartłomiej, Dariusz Majerek, Piotr Szyszka, Dariusz Wójcik, and Tomasz Rymarczyk. "Ultrasound tomography enhancement by signal feature extraction with modular machine learning method." PLOS ONE 19, no. 1 (2024): e0297496. http://dx.doi.org/10.1371/journal.pone.0297496.
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Robust and reliable diagnostic methods are desired in various types of industries. This article presents a novel approach to object detection in industrial or general ultrasound tomography. The key idea is to analyze the time-dependent ultrasonic signal recorded by three independent transducers of an experimental system. It focuses on finding common or related characteristics of these signals using custom-designed deep neural network models. In principle, models use convolution layers to extract common features of signals, which are passed to dense layers responsible for predicting the number of objects or their locations and sizes. Predicting the number and properties of objects are characterized by a high value of the coefficient of determination R2 = 99.8% and R2 = 98.4%, respectively. The proposed solution can result in a reliable and low-cost method of object detection for various industry sectors.
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Li, Kaylee Yaxuan, Yasha Iravantchi, Yichen Zhu, Hyunmin Park, and Alanson P. Sample. "HandSAW: Wearable Hand-based Event Recognition via On-Body Surface Acoustic Waves." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 9, no. 1 (2025): 1–29. https://doi.org/10.1145/3712276.
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Enabling computing systems to detect the objects that people hold and interact with provides valuable contextual information that has the potential to support a wide variety of mobile applications. However, existing approaches either directly instrument users' hands, which can reduce tactile sensation, or are limited in the types of objects and interactions they can detect. This work introduces HandSAW, a wireless wrist-worn device incorporating a Surface Acoustic Wave (SAW) sensor with enhanced bandwidth and signal-to-noise ratio while rejecting through-air sounds. The device features a sealed mass-spring diaphragm positioned on top of the sound port of a MEMS microphone, enabling it to capture SAWs generated by objects and through touch interaction events. This custom-designed wearable platform, paired with a real-time ML pipeline, can distinguish 20 passive object events with >99% per-user accuracy and a 91.6% unseen-user accuracy, as validated through a 16-participant user study. For devices that do not emit SAWs, our active tags enable HandSAW to detect those objects and transmit encoded data using ultrasonic signals. Ultimately, HandSAW provides an easy-to-implement, robust, and cost-effective means for enabling user-object interaction and activity detection.
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Azurmendi, Iker, Ekaitz Zulueta, Jose Manuel Lopez-Guede, and Manuel González. "Simultaneous Object Detection and Distance Estimation for Indoor Autonomous Vehicles." Electronics 12, no. 23 (2023): 4719. http://dx.doi.org/10.3390/electronics12234719.
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Object detection is an essential and impactful technology in various fields due to its ability to automatically locate and identify objects in images or videos. In addition, object-distance estimation is a fundamental problem in 3D vision and scene perception. In this paper, we propose a simultaneous object-detection and distance-estimation algorithm based on YOLOv5 for obstacle detection in indoor autonomous vehicles. This method estimates the distances to the desired obstacles using a single monocular camera that does not require calibration. On the one hand, we train the algorithm with the KITTI dataset, which is an autonomous driving vision dataset that provides labels for object detection and distance prediction. On the other hand, we collect and label 100 images from a custom environment. Then, we apply data augmentation and transfer learning to generate a fast, accurate, and cost-effective model for the custom environment. The results show a performance of mAP0.5:0.95 of more than 75% for object detection and 0.71 m of mean absolute error in distance prediction, which are easily scalable with the labeling of a larger amount of data. Finally, we compare our method with other similar state-of-the-art approaches.
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Kiran, Konakalla. "Automated Commission Calculation and Sales Quota Management in Salesforce: A Code-Driven Approach for Sales Efficiency." European Journal of Advances in Engineering and Technology 7, no. 12 (2020): 125–27. https://doi.org/10.5281/zenodo.14006043.
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This paper presents an approach to developing a commission calculator within Salesforce, utilizing custom objects, fields, Apex code, and process automation. The solution is designed to streamline commission calculations based on various sales criteria and ensure secure and scalable data management. By leveraging Salesforce’s capabilities and incorporating custom code where necessary, this approach enhances accuracy, efficiency, and transparency in sales commission management. The paper explores the architecture, coding techniques, and process automations employed to achieve this, while discussing key challenges, best practices, and potential enhancements for future scalability.
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coons, ginger “all-lower-case”, and Matt Ratto. "Grease pencils and the persistence of individuality in computationally produced custom objects." Design Studies 41 (November 2015): 126–36. http://dx.doi.org/10.1016/j.destud.2015.08.005.
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Sirisha, Ms Mamidi. "Automated Object Detection and Tracking for Construction Site Safety." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 23–29. https://doi.org/10.22214/ijraset.2024.65695.
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The construction industry is increasingly adopting digital solutions to enhance safety, efficiency, and productivity. This project leverages the YOLOv8 object detection model and ByteTrack algorithm to track and count objects on construction sites. The system enables automated monitoring of personnel, machinery, and safety equipment through video analysis, addressing critical challenges like occlusions and dynamic object interactions. A custom dataset tailored for construction environments ensures high accuracy in detecting safety-critical items, such as personal protective equipment (PPE). This approach demonstrates significant potential for real-time safety assessments and operational optimization, serving as a technological leap in construction practices.
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Tupelo-Schneck, Robert. "An Introduction to Cordra." Research Ideas and Outcomes 8 (October 12, 2022): e95966. https://doi.org/10.3897/rio.8.e95966.
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Cordra is a digital object server that can function as a key infrastructural piece in FAIR DO (findable, accessible, interoperable and reusable digital object) implementations. Cordra manages JSON records and payloads as typed digital objects identified by handles. Cordra is neither a database nor an indexer, but it integrates the two and provides a unified interface.Cordra is intended to support both quick prototyping as well as production systems.For prototyping, Cordra makes it easy to get up and running rapidly with a digital object server. A potential Cordra administrator can download Cordra and very quickly have a server which supports creation, search, and retrieval of digital objects with resolvable identifiers. The server supports Digital Object Interface Protocol (DOIP) and HTTP APIs out of the box, as well as an immediately usable prototype user interface. Cordra saves substantial development time as it comes with ready-made functionality ranging from user authentication and access control to information validation, enrichment, storing, and indexing. By default, Cordra is configured to store objects on the local file system of the machine and use embedded Apache Lucene for indexing. Simply by editing type definitions in Cordra's user interface, the administrator can start changing the behavior of the APIs and user interface in real time for experimentation, including adding custom operations.For production use, Cordra allows intensive extension and customization of the processes underlying the digital object server: how digital objects are stored and indexed, how they are validated and enriched, how users authenticate, when and to whom to give access to objects, and what custom operations can be performed. In production Cordra is run at scale, supporting high reliability and performance; among other options Cordra supports MongoDB and Amazon S3 for storage, and Elasticsearch and Apache Solr for indexing. By definition of the underlying types and operations, Cordra is intended to serve directly as the API backend for a production application.This talk will cover basic Cordra features as well as customization/configuration basics. Examples of current use will be shown, including the use of the Digital Object Interface Protocol (DOIP), for which Cordra serves as a reference implementation.Current users of Cordra include the Derivatives Service Bureau (DSB), which uses Cordra as part of its backend to manage the automated generation of International Securities Identification Numbers (ISINs) for OTC derivatives in the financial services sector; and the British Standard Institute (BSI) whose Identify service for construction product manufacturers aims to assign a Universal Persistent Identification Number (UPIN) "for every product that is specified and incorporated in a building structure". The DSB, Cordra users since 2017, has a production system with over 80 million identified digital objects which receives millions of searches each month. BSI.Identify has a system where Cordra's DOIP interface is directly accessible as the service's public API.
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Jacoski, Claudio Alcides, and Lissandro Machado Hoffmeister. "Potential use of BIM for automated updating of building materials values." Brazilian Journal of Operations & Production Management 15, no. 1 (2018): 35–43. http://dx.doi.org/10.14488/bjopm.2018.v15.n1.a4.
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This study proposes an artifact motivated by improved assertiveness in building design budgets. Building Information Modeling (BIM), with the structure of the parametric objects created in a file format with the Industry Foundation Classes (IFC) extension, can provide the data for the object, facilitating the design's control and monitoring process. Through the adoption of the IFC standard in the creation of these objects, the exchange of information between the tools of different software providers becomes viable, allowing interoperability between systems. This is a desired situation in the construction industry, which incurs significant losses due to this problem. An important condition that can significantly contribute to the update of the information of the objects and the budget process is the incorporation of the possibility of updating the value information (price) of the BIM objects that are shared in repositories (object libraries). In this context, this study presents an alternative to updating and retrieving the values of BIM objects based on the IFC standard. An artifact (web environment) was produced linked to a model to meet the proposed objective. This method is presented by computing services, enabling the automated retrieval of the object value between the owners, the price repository and also the designers. The performed tests reveal the practicality of its implementation, with no extensive knowledge of the IFC structure being necessary. It suffices to simply follow the fill out pattern of the custom properties in IFC, defined during the creation of the object. The submission of the construction design to the repository allows for the retrieval of the values and the quantification of objects present in the design. This process is carried out in a simple manner, maintaining the synchrony and traceability of the object with the designer and the owners of the objects making up the architectural and complementary design.
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Zhang, Yunbo, and Tsz Ho Kwok. "An interactive product customization framework for freeform shapes." Rapid Prototyping Journal 23, no. 6 (2017): 1136–45. http://dx.doi.org/10.1108/rpj-08-2016-0129.
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Purpose The purpose of this paper is to establish new computer-aided-design (CAD) framework to design custom product that is fabricated additive manufacturing (AM), which can produce complex three-dimensional (3D) object without additional tool or fixture. Additive manufacturing (AM) enables the fabrication of three-dimensional (3D) objects with complex shapes without additional tools and refixturing. However, it is difficult for user to use traditional computer-aided design tools to design custom products. Design/methodology/approach In this paper, the authors presented a design system to help user design custom 3D printable products based on some reference freeform shapes. The user can define and edit styling curves on the reference model using the interactive geometric operations for styling curve. Incorporating with the reference models, these curves can be converted into 3D printable models through the fabrication interface. Findings The authors tested their system with four design applications including a hollow patterned bicycle helmet, a T-rex with skin frame structure, a face mask with Voronoi patterns and an AM-specific night dress with hollow patterns. Originality/value The executable prototype of the presented design framework used in the customization process is publicly available.
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Young, J. S., S. R. Fox, and K. S. Anseth. "A Novel Device for Producing Three-Dimensional Objects." Journal of Manufacturing Science and Engineering 121, no. 3 (1999): 474–77. http://dx.doi.org/10.1115/1.2832705.
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This work describes a novel device for producing three-dimensional objects that has been developed using a liquid crystal display as a programmable, dynamic mask and visible light to initiate photopolymerization. This device has the potential to produce three-dimensional objects of comparable quality to the existing commercial devices, but in significantly less time. Additionally, capital, maintenance and operating costs are expected to be substantially lower than those for laser-based systems. The reduction in time and expense could expand this technology into the realm of custom part production and further increase the accessibility and usefulness of solid freeform fabrication and rapid prototyping.
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Ding, Dong, Zhengrong Deng, and Rui Yang. "YOLO-TC: An Optimized Detection Model for Monitoring Safety-Critical Small Objects in Tower Crane Operations." Algorithms 18, no. 1 (2025): 27. https://doi.org/10.3390/a18010027.
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Ensuring operational safety within high-risk environments, such as construction sites, is paramount, especially for tower crane operations where distractions can lead to severe accidents. Despite existing behavioral monitoring approaches, the task of identifying small yet hazardous objects like mobile phones and cigarettes in real time remains a significant challenge in ensuring operator compliance and site safety. Traditional object detection models often fall short in crane operator cabins due to complex lighting conditions, cluttered backgrounds, and the small physical scale of target objects. To address these challenges, we introduce YOLO-TC, a refined object detection model tailored specifically for tower crane monitoring applications. Built upon the robust YOLOv7 architecture, our model integrates a novel channel–spatial attention mechanism, ECA-CBAM, into the backbone network, enhancing feature extraction without an increase in parameter count. Additionally, we propose the HA-PANet architecture to achieve progressive feature fusion, addressing scale disparities and prioritizing small object detection while reducing noise from unrelated objects. To improve bounding box regression, the MPDIoU Loss function is employed, resulting in superior accuracy for small, critical objects in dense environments. The experimental results on both the PASCAL VOC benchmark and a custom dataset demonstrate that YOLO-TC outperforms baseline models, showcasing its robustness in identifying high-risk objects under challenging conditions. This model holds significant promise for enhancing automated safety monitoring, potentially reducing occupational hazards by providing a proactive, resilient solution for real-time risk detection in tower crane operations.
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Eldefrawy, Mahmoud, Scott A. King, and Michael Starek. "Partial Scene Reconstruction for Close Range Photogrammetry Using Deep Learning Pipeline for Region Masking." Remote Sensing 14, no. 13 (2022): 3199. http://dx.doi.org/10.3390/rs14133199.
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3D reconstruction is a beneficial technique to generate 3D geometry of scenes or objects for various applications such as computer graphics, industrial construction, and civil engineering. There are several techniques to obtain the 3D geometry of an object. Close-range photogrammetry is an inexpensive, accessible approach to obtaining high-quality object reconstruction. However, state-of-the-art software systems need a stationary scene or a controlled environment (often a turntable setup with a black background), which can be a limiting factor for object scanning. This work presents a method that reduces the need for a controlled environment and allows the capture of multiple objects with independent motion. We achieve this by creating a preprocessing pipeline that uses deep learning to transform a complex scene from an uncontrolled environment into multiple stationary scenes with a black background that is then fed into existing software systems for reconstruction. Our pipeline achieves this by using deep learning models to detect and track objects through the scene. The detection and tracking pipeline uses semantic-based detection and tracking and supports using available pretrained or custom networks. We develop a correction mechanism to overcome some detection and tracking shortcomings, namely, object-reidentification and multiple detections of the same object. We show detection and tracking are effective techniques to address scenes with multiple motion systems and that objects can be reconstructed with limited or no knowledge of the camera or the environment.
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Ebel, Henrik, and Peter Eberhard. "Cooperative transportation: realizing the promises of robotic networks using a tailored software/hardware architecture." at - Automatisierungstechnik 70, no. 4 (2022): 378–88. http://dx.doi.org/10.1515/auto-2021-0105.
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Abstract With cooperative transportation, the paper looks at a demanding problem from distributed robotics. At its heart, the proposed transportation scheme uses distributed model predictive control. Yet, distributed control alone does not suffice to solve the task. Thus, also distributed organization, a custom software architecture, simulation, and custom robotic hardware are dealt with, bridging the gap between distributed control theory and practical robotics. The robots are enabled to transport arbitrarily-shaped objects, automatically adapting to changing circumstances and numbers of robots.
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Natuskar, Sakshi. "Voice Assistant for Blind." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 3956–60. https://doi.org/10.22214/ijraset.2025.69183.
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Navigating safely through everyday environments can be extremely challenging for people with visual impairments. To address this, we developed a smart Blind Voice Assistant that uses the YOLOv8 object detection model to identify surrounding objects in real time. The system can detect both common and potentially dangerous items, estimate their approximate distance in steps, and immediately inform the user through voice feedback. This approach allows users to become more aware of their surroundings and make safer decisions as they move around. By training the model with both standard COCO data and a custom dataset including items like knives, pens, and bags, we ensure the assistant is tuned to recognize important objects in daily life. Overall, the assistant aims to empower visually impaired individuals by offering real-time environmental awareness and guided navigation.
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Calabrese, Bernardo, Ramiro Velázquez, Carolina Del-Valle-Soto, Roberto de Fazio, Nicola Ivan Giannoccaro, and Paolo Visconti. "Solar-Powered Deep Learning-Based Recognition System of Daily Used Objects and Human Faces for Assistance of the Visually Impaired." Energies 13, no. 22 (2020): 6104. http://dx.doi.org/10.3390/en13226104.
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This paper introduces a novel low-cost solar-powered wearable assistive technology (AT) device, whose aim is to provide continuous, real-time object recognition to ease the finding of the objects for visually impaired (VI) people in daily life. The system consists of three major components: a miniature low-cost camera, a system on module (SoM) computing unit, and an ultrasonic sensor. The first is worn on the user’s eyeglasses and acquires real-time video of the nearby space. The second is worn as a belt and runs deep learning-based methods and spatial algorithms which process the video coming from the camera performing objects’ detection and recognition. The third assists on positioning the objects found in the surrounding space. The developed device provides audible descriptive sentences as feedback to the user involving the objects recognized and their position referenced to the user gaze. After a proper power consumption analysis, a wearable solar harvesting system, integrated with the developed AT device, has been designed and tested to extend the energy autonomy in the different operating modes and scenarios. Experimental results obtained with the developed low-cost AT device have demonstrated an accurate and reliable real-time object identification with an 86% correct recognition rate and 215 ms average time interval (in case of high-speed SoM operating mode) for the image processing. The proposed system is capable of recognizing the 91 objects offered by the Microsoft Common Objects in Context (COCO) dataset plus several custom objects and human faces. In addition, a simple and scalable methodology for using image datasets and training of Convolutional Neural Networks (CNNs) is introduced to add objects to the system and increase its repertory. It is also demonstrated that comprehensive trainings involving 100 images per targeted object achieve 89% recognition rates, while fast trainings with only 12 images achieve acceptable recognition rates of 55%.
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PHADKE, Anuradha, Rucha VAIKAR, Avni KHETRAPAL, and Mehul VERMA. "Object Detection on Thermal Images: Performance of Yolov4 vs Yolov4 Tiny trained on Custom Datasets." Electrotehnica, Electronica, Automatica 72, no. 3 (2024): 53–61. http://dx.doi.org/10.46904/eea.23.72.3.1108006.
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The process of identifying and pinpointing the location of objects within an image or video is a crucial task in computer vision, known as object detection. While there has been significant progress in object detection using conventional optical RGB images, there has been comparatively less research done on object detection using thermal images. Thermal imaging has the advantage of being able to capture images in low light or even complete darkness, making it an attractive technology for surveillance applications. However, due to the scarcity of publicly available thermal image datasets, the development of object detectors specifically for thermal images has been hindered. In the proposed work a dataset of 2000 thermal images of three classes namely Humans, Dog, and Cats is collected using FLIR thermal camera. The YOLO (You Only Look Once) algorithm, specifically YOLOv4 and YOLOv4 tiny versions, are assessed for their performance to classify thermal images into three classes namely Human, Dog, and Cat. The findings demonstrate the potential of YOLOv4 for object detection on thermal images, especially when trained on large custom datasets. The results of this study may lead to the design of effective and efficient low-light vision systems that can be utilized in thermal imaging applications.
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Han, Shaolong, Shangrong Wang, Wenqi Liu, YongQiang Gu, and Yujie Zhang. "Swarm Intelligence-Enhanced Detection of Small Objects Using Key Point-Driven YOLO." International Journal of Swarm Intelligence Research 16, no. 1 (2025): 1–20. https://doi.org/10.4018/ijsir.368649.
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Traditional object detection methods, such as anchor-based YOLO variants, face challenges due to the irregular shapes and small sizes of these contaminants. This paper introduces a novel approach that leverages swarm Intelligence to enhance the performance of a keypoint-driven YOLO framework. By integrating keypoint detection with Boundary-Aware Vectors (BBAVectors) and utilizing swarm intelligence algorithms for model optimization, our approach improves the localization and identification of small, irregularly shaped non-metallic objects. By optimizing the feature extraction process through swarm-based techniques and incorporating keypoint-driven object detection, our model significantly boosts precision and recall compared to traditional methods. Evaluated on a custom dataset of fiber like materials, our approach achieves a mean Average Precision (mAP) of 92.9% at IoU 0.5, demonstrating strong performance in real-world applications.
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Jeong, Soo-Yeon, Junseok Kim, and Sun-Young Ihm. "The Design and Construction of a Grid Skyline for Custom-Built PC Recommendations Based on a Multi-Attribute Model." Designs 7, no. 5 (2023): 104. http://dx.doi.org/10.3390/designs7050104.
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In recent years, people have been buying custom-built PCs based on the performance they want and what they will use them for. However, there are many challenges for non-technical users when purchasing a custom-built PC. Not only is the terminology of computer devices unfamiliar to non-experts, but there are many specifications for different computer devices that need to be considered. Therefore, this paper proposes a method for recommending appropriate device models when purchasing custom-built PCs using a skyline. Because different computer devices have different specifications, we need a method that takes into account multiple attributes. Skyline querying is a technique that considers multiple attributes of an object and indexes them in order of user satisfaction. A grid skyline is a technique that uses a grid-based partitioning technique to reduce the number of calculations of the dominance relationship between objects in the existing skyline technique, thus reducing the index construction time. We measured the similarity between the results of the grid skyline and the leaderboard for each model of computer device. As a result of this experiment, compared to the leaderboard categorized by model of computer device, the average score was 88 out of 100, which was similar to the actual leaderboard.
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Rey, Thomas, Julien Moras, Alexandre Eudes, and Antoine Manzanera. "Real-time visual pose estimation: from BOP objects to custom drone — A journey." Mechatronics 109 (August 2025): 103339. https://doi.org/10.1016/j.mechatronics.2025.103339.
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Lenkov, E. S. "DEVELOPMENT OF WAREHOUSE AND RESOURCE MODELING METHODS WEAPONS AND MILITARY EQUIPMENT GROUP FOR USER." Collection of scientific works of the Military Institute of Kyiv National Taras Shevchenko University, no. 74 (2022): 31–41. http://dx.doi.org/10.17721/2519-481x/2022/74-04.
Full textAbstract:
For creation a custom model of the group of armaments and military equipment, it’s proposed to enter real data on the existing objects, that are part of this group in the database of models. The technology of creating a custom grouping is no different from the technology of creating a new grouping discussed earlier. In fact, the user grouping model is initially created simply as a new grouping, and all regulatory resource parameters of all objects must be entered into the database exactly as it’s done for a virtual grouping. Differences begin only after saving the grouping in the model database. After saving a new group, you can work as a virtual group, generating and saving its various variants, or save it as a custom group. In the latter case, you can no longer experiment with the group (create any number of options for it and explore them), but can only make forecast and planned calculations in the same way as you can for saved versions of virtual groups. In simulation mode, working with a group of users is no different from working with virtual groups. The only difference is that you need to choose not from two forecasting modes, but from four: regulatory planning and user planning, both with the conditions of delivery of new facilities and without them. In the article the research of model groupings of objects of armaments and military equipment of old, new and balanced taking into account deliveries of new samples is carried out. The modeling procedure in the group user mode includes modeling the processes of spending and replenishing the resource in order to obtain the necessary schedule and edit data on all objects of the group; editing the plan of repairs and deliveries of new objects. The modeling in the mode of normative planning for objects of conditional types Tin-0 and Tin-1 is carried out. This simulation showed that the first repair is planned for 01.2023 and write-off on 03.2031. The similar results were obtained for the conditions with the delivery of new facilities. The rather significant efficiency of the developed methodology of the research models of armaments and military equipment grouping for using is confirmed in practice.