Relevant bibliographies by topics / Object detection.Convolutional neural networks. YOLO. Deep learning.Computer vision

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'Object detection.Convolutional neural networks. YOLO. Deep learning.Computer vision'

Author: Grafiati
Published: 5 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Object detection.Convolutional neural networks. YOLO. Deep learning.Computer vision"

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Soppari, Dr Kavitha, D. Varun, Eedula Rithvik, and Manchala Anudeep. "Portable Object Detection in Real-Time." International Scientific Journal of Engineering and Management 04, no. 02 (2025): 1–11. https://doi.org/10.55041/isjem02269.

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Portable Object Detection in Real-Time is a computer vision- based project that enables the identification and classification of objects using a laptop's built-in camera. The system leverages deep learning techniques, specifically convolutional neural networks (CNNs) and pre-trained models such as YOLO (You Only Look Once) or SSD (Single Shot MultiBox Detector), to perform efficient and accurate object detection. The project aims to provide a lightweight and portable solution without requiring external hardware, making it accessible for various applications such as security monitoring, automat
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M., Chinnarao R. Goutham Sai Kalyan T. Naga Pravallika B. Srinivas. "Object Detection Using Yolo And Tensor Flow." International Journal in Engineering Sciences 1, no. 1 (2024): 13–23. https://doi.org/10.5281/zenodo.11825059.

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Object detection methods aim to identify all target objects in the target image and determine the categories and position information in order to achieve machine vision understanding. Numerous approaches have been proposed to solve this problem, mainly inspired by methods of computer vision and deep learning. However, existing approaches always perform poorly for the detection of small, dense objects, and even fail to detect objects with random geometric transformations. In this study, we compare and analyse mainstream object detection algorithms and propose a multi-scaled deformable convoluti
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Tiwari, Shashank. "Advanced Two Stage AI Technique for Object Detection." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47821.

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Object detection in computer vision uses AI, mainly deep learning, to identify and locate objects in images or videos. It involves an AI system that spots various objects, determines their type, and marks their positions with bounding boxes. Built on advanced deep learning models like Convolutional Neural Networks (CNNs), YOLO, or Faster R-CNN, it excels in real-time detection. Trained on large datasets like COCO, it recognizes diverse objects across different scenes. It tackles challenges investigates challenges like varying object sizes, scales, lighting, and occlusion using techniques such
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R, Prithvi Raj, Rohith M, Ravichandra A R, and Shafien Ulla Khan. "IMAGE CHARACTER RECOGNITION USING CONVOLUTIONAL NEURAL NETWORKS." International Research Journal of Computer Science 9, no. 8 (2022): 304–11. http://dx.doi.org/10.26562/irjcs.2022.v0908.29.

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Object Recognition is a challenging and exciting task of computer Vision. Object recognition is to describe a collection of related computer vision tasks which involves identifying objects in images. Image classification involves predicting the class of objects in an image. Object localization refers to identifying the location of objects in an image. Image Annotation in Machine Learning is the process of creating bounding boxes around the localized images. Now with the advance of deep learning and neural networks, we can finally tackle these problems without coming up with various heuristics
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Park, Hee-Mun, and Jin-Hyun Park. "YOLO Network with a Circular Bounding Box to Classify the Flowering Degree of Chrysanthemum." AgriEngineering 5, no. 3 (2023): 1530–43. http://dx.doi.org/10.3390/agriengineering5030094.

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Detecting objects in digital images is challenging in computer vision, traditionally requiring manual threshold selection. However, object detection has improved significantly with convolutional neural networks (CNNs), and other advanced algorithms, like region-based convolutional neural networks (R-CNNs) and you only look once (YOLO). Deep learning methods have various applications in agriculture, including detecting pests, diseases, and fruit quality. We propose a lightweight YOLOv4-Tiny-based object detection system with a circular bounding box to accurately determine chrysanthemum flower h
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Muhammad Zafar Ul Haq, Mukkaram Baig, Ayaan Zaman Khattak, Faizan Asghar, Muhammad Zunnurain Hussain, and Muhammad Zulkifl Hasan. "Redefining Object Detection: Harnessing the Full Potential of YOLO." Annual Methodological Archive Research Review 3, no. 1 (2025): 68–80. https://doi.org/10.63075/r165ne08.

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Recently, there has been a notable use of deep learning methodologies, namely convolutional neural networks (CNNs), in computer vision, specifically about the significant matter of object recognition. The "You Only Look Once" (YOLO) technique is a strategy that offers a rapid and dependable approach for detecting objects in both static and dynamic visual content. This article presents a comprehensive overview of YOLO, including its historical context, architectural design, and performance evaluation on many widely accepted benchmarks within the industry. In addition, we identify the study's li
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Narendra, Joglekar. "Human Detector & Counting." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48230.

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Abstract - Human detection and counting are crucial tasks in computer vision, with applications in security surveillance, crowd monitoring, retail analytics, and smart city planning. This paper explores various approaches to human detection and counting, including traditional image processing techniques and modern deep learning-based methods such as Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO). Challenges such as occlusion, varying lighting conditions, and real-time processing constraints are addressed. The study also highlights the integration of human detection models w
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Ali, Mahmoud Atta Mohammed. "Advancing Crowd Object Detection: A Review of YOLO, CNN, and Vision Transformers Hybrid Approach." International Journal for Research in Applied Science and Engineering Technology 12, no. 6 (2024): 1240–68. http://dx.doi.org/10.22214/ijraset.2024.63293.

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Abstract: One of the most basic and difficult areas of computer vision and image understanding applications is still object detection. Deep neural network models and enhanced object representation have led to significant progress in object detection. This research investigates in greater detail how object detection has changed in the recent years in the deep learning age. We provide an overview of the literature on a range of cutting-edge object identification algorithms and the theoretical underpinnings of these techniques. Deep learning technologies are contributing to substantial innovation
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Gururaj, Vaishnavi, Shriya Varada Ramesh, Sanjana Satheesh, Ashwini Kodipalli, and Kusuma Thimmaraju. "Analysis of deep learning frameworks for object detection in motion." International Journal of Knowledge-based and Intelligent Engineering Systems 26, no. 1 (2022): 7–16. http://dx.doi.org/10.3233/kes-220002.

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Object detection and recognition is a computer vision technology and is considered as one of the challenging tasks in the field of computer vision. Many approaches for detection have been proposed in the past. AIM: This paper is mainly aiming to discuss the existing detection and classification techniques of Deep Convolutional Neural Networks (CNN) with an importance placed on highlighting the training and accuracy of the different CNN models. METHODS: In the proposed work, Faster RCNN, YOLO and SSD are used to detect helmets. OUTCOME: The survey says MobileNets has higher accuracy when compar
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Kalshetti, Mallinath. "Object Detection and Recognition Using Image Processing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem30262.

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Object detection and recognition are critical problems in computer vision, with numerous applications in areas such as surveillance, autonomous systems, and medical imaging. This study provides a comprehensive overview of object detection and recognition utilizing image processing methods. Object detection is the process of finding and locating objects inside picture or video frames. Traditional approaches were based on handcrafted features and classifiers, but recent advancements in deep learning, particularly Convolutional Neural Networks (CNNs), have changed the discipline. Architectures su
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Dissertations / Theses on the topic "Object detection.Convolutional neural networks. YOLO. Deep learning.Computer vision"

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Lamberti, Lorenzo. "A deep learning solution for industrial OCR applications." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2019. http://amslaurea.unibo.it/19777/.

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This thesis describes a project developed throughout a six months internship in the Machine Vision Laboratory of Datalogic based in Pasadena, California. The project aims to develop a deep learning system as a possible solution for industrial optical character recognition applications. In particular, the focus falls on a specific algorithm called You Only Look Once (YOLO), which is a general-purpose object detector based on convolutional neural networks that currently offers state-of-the-art performances in terms of trade-off between speed and accuracy. This algorithm is indeed well known fo
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Norrstig, Andreas. "Visual Object Detection using Convolutional Neural Networks in a Virtual Environment." Thesis, Linköpings universitet, Datorseende, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-156609.

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Visual object detection is a popular computer vision task that has been intensively investigated using deep learning on real data. However, data from virtual environments have not received the same attention. A virtual environment enables generating data for locations that are not easily reachable for data collection, e.g. aerial environments. In this thesis, we study the problem of object detection in virtual environments, more specifically an aerial virtual environment. We use a simulator, to generate a synthetic data set of 16 different types of vehicles captured from an airplane. To study
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Dickens, James. "Depth-Aware Deep Learning Networks for Object Detection and Image Segmentation." Thesis, Université d'Ottawa / University of Ottawa, 2021. http://hdl.handle.net/10393/42619.

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The rise of convolutional neural networks (CNNs) in the context of computer vision has occurred in tandem with the advancement of depth sensing technology. Depth cameras are capable of yielding two-dimensional arrays storing at each pixel the distance from objects and surfaces in a scene from a given sensor, aligned with a regular color image, obtaining so-called RGBD images. Inspired by prior models in the literature, this work develops a suite of RGBD CNN models to tackle the challenging tasks of object detection, instance segmentation, and semantic segmentation. Prominent architectur
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Schennings, Jacob. "Deep Convolutional Neural Networks for Real-Time Single Frame Monocular Depth Estimation." Thesis, Uppsala universitet, Avdelningen för systemteknik, 2017. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-336923.

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Vision based active safety systems have become more frequently occurring in modern vehicles to estimate depth of the objects ahead and for autonomous driving (AD) and advanced driver-assistance systems (ADAS). In this thesis a lightweight deep convolutional neural network performing real-time depth estimation on single monocular images is implemented and evaluated. Many of the vision based automatic brake systems in modern vehicles only detect pre-trained object types such as pedestrians and vehicles. These systems fail to detect general objects such as road debris and roadside obstacles. In s
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Melcherson, Tim. "Image Augmentation to Create Lower Quality Images for Training a YOLOv4 Object Detection Model." Thesis, Uppsala universitet, Signaler och system, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-429146.

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Research in the Arctic is of ever growing importance, and modern technology is used in news ways to map and understand this very complex region and how it is effected by climate change. Here, animals and vegetation are tightly coupled with their environment in a fragile ecosystem, and when the environment undergo rapid changes it risks damaging these ecosystems severely.  Understanding what kind of data that has potential to be used in artificial intelligence, can be of importance as many research stations have data archives from decades of work in the Arctic. In this thesis, a YOLOv4 object d
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Carletti, Angelo. "Development of a machine learning algorithm for the automatic analysis of microscopy images in an in-vitro diagnostic platform." Master's thesis, Alma Mater Studiorum - Università di Bologna, 2021.

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In this thesis we present the development of machine learning algorithms for single cell analysis in an in-vitro diagnostic platform for Cellply, a startup that operates in precision medicine. We researched the state of the art of deep learning for biomedical image analysis, and we analyzed the impact that convolutional neural networks have had in object detection tasks. Then we compared neural networks that are currently used for cell detection, and we chose the one (i.e. Stardist) that is able to perform a more efficient detection also in a crowded cells context. We could train models usi
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Marko, Peter. "Detekce objektů v laserových skenech pomocí konvolučních neuronových sítí." Master's thesis, Vysoké učení technické v Brně. Fakulta informačních technologií, 2021. http://www.nusl.cz/ntk/nusl-445509.

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This thesis is aimed at detection of lines of horizontal road markings from a point cloud, which was obtained using mobile laser mapping. The system works interactively in cooperation with user, which marks the beginning of the traffic line. The program gradually detects the remaining parts of the traffic line and creates its vector representation. Initially, a point cloud is projected into a horizontal plane, crating a 2D image that is segmented by a U-Net convolutional neural network. Segmentation marks one traffic line. Segmentation is converted to a polyline, which can be used in a geo-inf
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Chen, Hao. "Efficient Fully-Convolutional Networks for Image Perception." Thesis, 2021. http://hdl.handle.net/2440/130111.

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Neural architecture search is widely applied to design networks to outperform manually designed architectures. However, it is not trivial to be directly applied to challenging perception tasks such as object detection since previous methods often rely on manually designed complex operations such as RoI pooling and RCNN heads. Thus, we look for universal fully-convolutional representations for perception tasks, which are easy to optimise and deploy because of their sim ple structures. They perform well on dense prediction tasks such as semantic segmentation, where the networks consist of a back
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Pinto, Tiago Alexandre Barbosa. "Object detection with artificial vision and neural networks for service robots." Master's thesis, 2018. http://hdl.handle.net/1822/62251.

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Dissertação de mestrado em Engenharia Eletrónica Industrial e Computadores<br>This dissertation arises from a major project that consists on developing a domestic service robot, named CHARMIE (Collaborative Home Assistant Robot by Minho Industrial Electronics), to cooperate and help on domestic tasks. In general, the project aims to implement artificial intelligence in the whole robot. The main contribution of this dissertation is the development of the vision system, with artificial intelligence, to classify and detect, in real time, the objects represented on the environment that the robot
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Albuquerque, Carina Isabel Andrade. "Convolutional neural networks for cell detection and counting : a case study of human cell quantification in zebrafish xenografts using deep learning object detection techniques." Master's thesis, 2019. http://hdl.handle.net/10362/62425.

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Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics<br>Deep learninghad,inrecentyears,becamethestateofthearttodealwithcomputer vision problems.Onthecomputervisionresearchfield,objectdetectionisatechnique thatallowstolocalizeandclassifyoneormoreobjectsinaninputimage.Thisapproach can beappliedtoseveraltasksandproblems,ascellcountinginmedicalimaging,as proposed inthisthesis. Cellcountingisafrequentlyneededtaskinseveralmedicaltypesofresearch,butof- ten stillmademanuallyduetoseveralconstraints.Theautomationofthisprocesscan
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Book chapters on the topic "Object detection.Convolutional neural networks. YOLO. Deep learning.Computer vision"

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Li, Kaidong, Wenchi Ma, Usman Sajid, Yuanwei Wu, and Guanghui Wang. "Object Detection with Convolutional Neural Networks." In Deep Learning in Computer Vision. CRC Press, 2020. http://dx.doi.org/10.1201/9781351003827-2.

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Hoang, Minh Long. "Deep Learning in Object Detection for the Autonomous Car." In Artificial Intelligence Development in Sensors and Computer Vision for Health Care and Automation Application. BENTHAM SCIENCE PUBLISHERS, 2024. https://doi.org/10.2174/9789815313055124010007.

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This chapter explores the practical application of artificial intelligence (AI) techniques in self-driving cars, mainly focusing on object recognition. Deep learning has emerged as a powerful tool for object detection, playing a crucial role in processing data from lidar, radar, and video cameras. These three technologies are essential components of autonomous vehicles, providing critical obstacle information that enables the automatic system to execute appropriate actions based on the received data. We delve into three advanced techniques that enhance object detection capabilities in autonomo
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Gandrapu Satya Sai Surya Subrahmanya Venkata Krishna Mohan, Mahammad Firose Shaik, G. Usandra Babu, Manikandan Hariharan, and Kiran Kumar Patro. "Deep Learning-Powered Visual Augmentation for the Visually Impaired." In Blockchain-Enabled Internet of Things Applications in Healthcare: Current Practices and Future Directions. BENTHAM SCIENCE PUBLISHERS, 2025. https://doi.org/10.2174/9789815305210125010013.

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The interdisciplinary convergence of computer vision and object detection is pivotal for advancing intelligent image analysis. This research surpasses conventional object recognition methodologies by delving into a more nuanced understanding of images, akin to human visual comprehension. It explores deep learning and established object detection systems such as convolutional neural networks (CNN), Region-based CNN (R-CNN), and you only look once (YOLO). The proposed model excels in realtime object recognition, outperforming its predecessors, as previous systems typically detect only a limited
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Deepan, Dr P. "ANOMALY HUNTER: YOLOV3 ADVANCED DEEP LEARNING MODEL FOR HUMAN ABNORMAL DETECTION." In HEALTHCARE APPLICATIONS IN COMPUTER VISION AND DEEP LEARNING TECHNIQUES. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/nbennurch240.

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The identification of aberrant human behaviour is an important component in the process of protecting the health and safety of persons as well as the preservation of the safe environment in public areas. Advanced algorithms such as YOLO (You Only Look Once) and Convolutional Neural Networks (CNN) are utilised by emerging technologies such as human behaviour and anomaly detection. These algorithms are utilised to extract characteristics, manage temporal relationships, and enhance the accuracy and efficiency of human behaviour detection systems. Examples of such algorithms are the YOLO and CNN a
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Xiao, Bingjie, Minh Nguyen, and Wei Qi Yan. "A Mixture Model for Fruit Ripeness Identification in Deep Learning." In Handbook of Research on AI and ML for Intelligent Machines and Systems. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-9999-3.ch016.

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Visual object detection is a foundation in the field of computer vision. Since the size of visual objects in an images is various, the speed and accuracy of object detection are the focus of current research projects in computer vision. In this book chapter, the datasets consist of fruit images with various maturity. Different types of fruit are divided into the classes “ripe” and “overripe” according to the degree of skin folds. Then the object detection model is employed to automatically classify different ripeness of fruits. A family of YOLO models are representative algorithms for visual o
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Mane, D. T., and U. V. Kulkarni. "A Survey on Supervised Convolutional Neural Network and Its Major Applications." In Deep Learning and Neural Networks. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch059.

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With the advances in the computer science field, various new data science techniques have been emerged. Convolutional Neural Network (CNN) is one of the Deep Learning techniques which have captured lots of attention as far as real world applications are considered. It is nothing but the multilayer architecture with hidden computational power which detects features itself. It doesn't require any handcrafted features. The remarkable increase in the computational power of Convolutional Neural Network is due to the use of Graphics processor units, parallel computing, also the availability of large
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Madeshwaran, Sivakumar, and M. Govindarajan. "Deep Learning Architectures for Image Processing Including Convolutional Neural Networks and Generative Adversarial Networks." In Image Processing Techniques and its Applications in Computer Vision and Artificial Intelligence. RADemics Research Institute, 2024. https://doi.org/10.71443/9788197933660-09.

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This book chapter provides a comprehensive exploration of deep learning architectures and their transformative impact on image processing, emphasizing the pivotal roles of Convolutional Neural Networks (CNNs) and Generative Adversarial Networks (GANs). The chapter begins with a historical context, tracing the evolution of image processing techniques from traditional algorithms to cutting-edge deep learning approaches. It delves into fundamental concepts, highlighting advancements in CNNs and emerging trends such as self-supervised learning. Additionally, the chapter examines the practical appl
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Ravikumar, Aswathy, and Harini Sriraman. "Understanding Convolutional Neural Network With TensorFlow." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-8531-6.ch003.

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In academia and business, deep-learning-based models have exhibited extraordinary performance over the last decade. The learning potential of Convolutional Neural Networks (CNNs) derives from a combination of several feature extraction levels that completely use a vast quantity of input. CNN is an important technique for tackling computer vision issues, although the theories behind its processing efficacy are not yet completely understood. CNN has achieved cutting-edge performance on a variety of datasets in computer vision applications like remote sensing, medical image categorization, facial
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Tuzova, Lyudmila N., Dmitry V. Tuzoff, Sergey I. Nikolenko, and Alexey S. Krasnov. "Teeth and Landmarks Detection and Classification Based on Deep Neural Networks." In Computational Techniques for Dental Image Analysis. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6243-6.ch006.

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In the recent decade, deep neural networks have enjoyed rapid development in various domains, including medicine. Convolutional neural networks (CNNs), deep neural network structures commonly used for image interpretation, brought the breakthrough in computer vision and became state-of-the-art techniques for various image recognition tasks, such as image classification, object detection, and semantic segmentation. In this chapter, the authors provide an overview of deep learning algorithms and review available literature for dental image analysis with methods based on CNNs. The present study i
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Gupta, Abhilasha, Krishna Joshi, and Umesh Diwedi. "Image and its Coordinates Detection in Convolution Neural Network Using YOLO Framework." In Artificial Intelligence and Communication Technologies, 2023rd ed. Soft Computing Research society, 2023. http://dx.doi.org/10.52458/978-81-955020-5-9-86.

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Computer vision is a field that deals with high level under- standing from digital Images. It provides new directions for making machines attentive and responsive to man. In this paper we are propos- ing an approach that is used for image detection. Face detection is a major problem in this area and many dataset are created for the pur- pose of computer vision. We have various deep learning models like convolutional neural network, recurrent neural network etc. But among all, deep convolutional neural networks are the best model for finding patterns from images. In the same direction we design
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Conference papers on the topic "Object detection.Convolutional neural networks. YOLO. Deep learning.Computer vision"

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Marchuk, Andrii. "YOLO ALGORITHM USING IN DECISION SUPPORT SYSTEMS BASED ON THE USE OF NEURAL NETWORKS." In 17th IC Measurement and Control in Complex Systems. VNTU, 2024. https://doi.org/10.31649/mccs2024.5-07.

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In recent years, computer technologies, particularly computer vision technologies, have played an increasingly important role in medical diagnostics, including otoscopy—the process of visualizing the external auditory canal and tympanic membrane to detect ear pathologies. However, otoscopic images have several characteristics that complicate automatic classification, such as variations in lighting, orientation, and the presence of artifacts. These factors necessitate the development of reliable deep learning models for accurate disease classification. This paper examines modern approaches to a
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Deakyne, Alex, Erik Gaasedelen, and Paul A. Iaizzo. "A Deep Learning Approach for the Automatic Identification of the Left Atrium Within CT Scans." In 2019 Design of Medical Devices Conference. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/dmd2019-3282.

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Recent advancements in deep learning have led to the possibility of increased performance in computer vision tools. A major development has been the usage of Convolutional Neural Networks (CNN) for automatically detecting features within a given image. Architectures such as YOLO1 have obtained incredibly high performances for the real-time detection of every-day objects within images. However to date, there have been few reports of deep learning applied to detect anatomical features within CT scans; especially those within the cardiovascular space. We propose here an automatic anatomical featu
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Abdelaziem, Osama Elsayed, Ahmed Ahmed Gawish, and Sayed Fadel Farrag. "Application of Computer Vision in Diagnosing Water Production Mechanisms in Oil Wells." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211804-ms.

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Abstract Diagnostic plots, introduced by K.S. Chan, are widely used to determine excessive water production mechanisms. In this paper, we introduce a computer vision model that is capable of segmenting and identifying multiple Chan signatures per plot, for the sake of surveillance and early screening, given that wells could exhibit diverse mechanisms throughout their lifecycle. As deep learning demands a vast amount of information, we start our workflow by building a dataset of 10,000 publicly available oil wells that have experienced varying water production mechanisms and annotating them. Ne
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Kasera, Shubham, Ajay Waghumbare, Sahil Mahajan, and Upasna Singh. "Moving Object Detection, Tracking and Range Estimation in Infrared Videos using Deep Learning." In 2nd International Conference on Emerging Applications of Artificial Intelligence, Machine Learning and Cybersecurity. AIJR Publisher, 2025. https://doi.org/10.21467/proceedings.178.28.

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Infrared video technology for moving Object Detection, Tracking and Range Estimation has become a pivotal tool in various fields such as video surveillance, infrared guidance, Unmanned Aerial Vehicle (UAV) based monitoring and autonomous vehicle systems to medical imaging and environmental monitoring. Detecting and estimating the range of a moving object in an infrared video is a critical task with applications in target tracking, obstacle avoidance, and 3D scene reconstruction. This abstract highlight the key aspects of R&amp;D in the field of detecting and estimation of objects tracking and
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Yang, Ru, and Ping Guo. "OF-NET: Deep-Learning Based Sub-Pixel Optical Flow Estimation With Multi-Scale Convolutional Neural Network." In ASME 2020 15th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/msec2020-8517.

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Abstract Object motion trajecting using computer vision is a technology enabler for various smart manufacturing systems. Sub-pixel displacement estimation is still unsatisfactory with the existing tracking algorithms. In this paper, we extend the popular computer vision task, optical flow, to solve the small displacement detection problem. Since conventional optical flow methods have weakness in robustness and poor performance at the boundary region, convolutional neural networks (CNNs) based approach has been developed to solve optical flow problems. We construct a new multi-scale CNN, OF-NET
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Banjanović-Mehmedović, Lejla, Anel Husaković, Azra Gurdić Ribić, Naser Prljača, and Isak Karabegović. "Advancements in Robotic Intelligence: The Role of Computer Vision, DRL, Transformers and LLMs." In Artificial Intelligence in Industry 4.0: The future that comes true. Academy of Sciences and Arts of Bosnia and Herzegovina, 2024. http://dx.doi.org/10.5644/pi2024.215.05.

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In recent advancements in robotics, Artificial Intelligence (AI) methods such as Deep Learning, Deep Reinforcement Learning (DRL), Transformers, and Large Language Models (LLMs) have significantly enhanced robotic capabilities. Key AI models driving advancements in robotic vision include Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), the DEtection Transformers (DETR), the YOLO family of algorithms, segmentation techniques, and 3D vision technologies. Deep Reinforcement Learning (DRL), an AI technique where agents learn optimal behaviors through trial and error interactions w
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Faria, Matheus Prado Prandini, Rita Maria Silva Julia, and Lídia Bononi Paiva Tomaz. "Investigating Learning Methods and Environment Representation in the Construction of Player Agents: Application on FIFA Game." In Anais Estendidos do Simpósio Brasileiro de Games e Entretenimento Digital. Sociedade Brasileira de Computação, 2021. http://dx.doi.org/10.5753/sbgames_estendido.2021.19744.

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The objective behind this study is to investigate Machine Learning (ML) techniques combined with methods from Computer Vision (CV) for state representation by images, to produce agents capable of solving problems, in real time, in environments with complex properties. Such difficulties require agents to be highly efficient in their learning (and, consequently, decision-making) and environmental perception processes, without which they will not be successful. The digital game FIFA - soccer simulator - is used as a case study because it represents a realistic and challenging environment. The ML
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Jagtap, Pramod Prakash, Shreeja Kale, and Rakesh Mahali. "Enhancing Cargo Transportation Using Intelligent Systems for Better Logistic Management." In Symposium on International Automotive Technology. SAE International, 2024. http://dx.doi.org/10.4271/2024-26-0183.

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&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Efficient cargo transportation plays a crucial role in logistics management and supply chain operations. Accurately detecting and utilizing cargo space within vehicles is vital for maximizing transport capacity, minimizing costs, and optimizing resource allocation and time management. This research paper focuses on enhancing cargo utilization using intelligent systems to improve logistics management. The major research is on developing a system that combines computer vision algorithms and intelligent systems to detect an
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