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Journal articles on the topic 'Indoor Object Detection'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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1

P S, Akash, and Preethi Thomas. "Indoor Object Detection for Blind." International Journal of Science and Research (IJSR) 14, no. 4 (2025): 1310–15. https://doi.org/10.21275/sr25417001126.

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Wang, Jie, Hong Zhao, Yuxi Huang, and Baotong Zhang. "Improved YOLOv8 Algorithm for Indoor Object Detection." Journal of Physics: Conference Series 3024, no. 1 (2025): 012037. https://doi.org/10.1088/1742-6596/3024/1/012037.

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Abstract To address the challenges of missed detections and false positives in indoor object detection tasks caused by target clustering and occlusion, this study proposes an advanced indoor object detection algorithm, SRSE-YOLOv8. A novel indoor object detection algorithm, SRSE-YOLOv8, is proposed to address missed and false detections caused by object clustering and occlusion in indoor scenes. First, an additional smaller detection head is introduced to enhance the capture of small objects and improve feature extraction capabilities. Then, RFAConv is incorporated to achieve input-adaptive ke

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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

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Fan, Zhipeng, Wanglong Mei, Wei Liu, Ming Chen, and Zeguo Qiu. "I-DINO: High-Quality Object Detection for Indoor Scenes." Electronics 13, no. 22 (2024): 4419. http://dx.doi.org/10.3390/electronics13224419.

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Object Detection in Complex Indoor Scenes is designed to identify and categorize objects in indoor settings, with applications in areas such as smart homes, security surveillance, and home service robots. It forms the basis for advanced visual tasks including visual question answering, video description generation, and instance segmentation. Nonetheless, the task faces substantial hurdles due to background clutter, overlapping objects, and significant size differences. To tackle these challenges, this study introduces an indoor object detection approach utilizing an enhanced DINO framework. To

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Wang, Li, Ruifeng Li, Jingwen Sun, et al. "Multi-View Fusion-Based 3D Object Detection for Robot Indoor Scene Perception." Sensors 19, no. 19 (2019): 4092. http://dx.doi.org/10.3390/s19194092.

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To autonomously move and operate objects in cluttered indoor environments, a service robot requires the ability of 3D scene perception. Though 3D object detection can provide an object-level environmental description to fill this gap, a robot always encounters incomplete object observation, recurring detections of the same object, error in detection, or intersection between objects when conducting detection continuously in a cluttered room. To solve these problems, we propose a two-stage 3D object detection algorithm which is to fuse multiple views of 3D object point clouds in the first stage

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Hu, JianSheng, JunJie Ma, Bin Xiao, and Rui Zhang. "Improved Lightweight YOLOv3 model for Target Detection Algorithm." Journal of Physics: Conference Series 2370, no. 1 (2022): 012029. http://dx.doi.org/10.1088/1742-6596/2370/1/012029.

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When detecting small objects in interior situations, the classic object detection algorithm performs poorly in terms of real-time detection task and high precision detection task. This paper suggests an optimized tiny-YOLOv3-Shufflenetv2 light-weight model based on indoor scenes. The scheme adopts the fusion light-weight network which combines ShuffleNetv2 and YOLOv3, it reduces the complexity of the model to meet the lightweight requirements while ensuring good detection results for deployment to mobile robots. Also in this paper, an indoor small target object dataset, indoor-2022, is created

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Kabir, Raihan, Yutaka Watanobe, Md Rashedul Islam, Keitaro Naruse, and Md Mostafizer Rahman. "Unknown Object Detection Using a One-Class Support Vector Machine for a Cloud–Robot System." Sensors 22, no. 4 (2022): 1352. http://dx.doi.org/10.3390/s22041352.

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Inter-robot communication and high computational power are challenging issues for deploying indoor mobile robot applications with sensor data processing. Thus, this paper presents an efficient cloud-based multirobot framework with inter-robot communication and high computational power to deploy autonomous mobile robots for indoor applications. Deployment of usable indoor service robots requires uninterrupted movement and enhanced robot vision with a robust classification of objects and obstacles using vision sensor data in the indoor environment. However, state-of-the-art methods face degraded

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Yu, Hang, Jinhe Su, Yingchao Piao, et al. "Refined Voting and Scene Feature Fusion for 3D Object Detection in Point Clouds." Computational Intelligence and Neuroscience 2022 (December 29, 2022): 1–15. http://dx.doi.org/10.1155/2022/3023934.

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An essential task for 3D visual world understanding is 3D object detection in lidar point clouds. To predict directly bounding box parameters from point clouds, existing voting-based methods use Hough voting to obtain the centroid of each object. However, it may be difficult for the inaccurately voted centers to regress boxes accurately, leading to the generation of redundant bounding boxes. For objects in indoor scenes, there are several co-occurrence patterns for objects in indoor scenes. Concurrently, semantic relations between object layouts and scenes can be used as prior context to guide

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Heikel, Edvard, and Leonardo Espinosa-Leal. "Indoor Scene Recognition via Object Detection and TF-IDF." Journal of Imaging 8, no. 8 (2022): 209. http://dx.doi.org/10.3390/jimaging8080209.

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Indoor scene recognition and semantic information can be helpful for social robots. Recently, in the field of indoor scene recognition, researchers have incorporated object-level information and shown improved performances. This paper demonstrates that scene recognition can be performed solely using object-level information in line with these advances. A state-of-the-art object detection model was trained to detect objects typically found in indoor environments and then used to detect objects in scene data. These predicted objects were then used as features to predict room categories. This pap

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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 robo

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Su, F., Y. Liang, Z. Gang, et al. "OBJECT DETECTION AND CLASSIFICATION FROM CLUTTERED LARGE-SCALE INDOOR SCENE VIA ANCHOR-BASED GRAPH." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences V-2-2020 (August 3, 2020): 289–96. http://dx.doi.org/10.5194/isprs-annals-v-2-2020-289-2020.

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Abstract. Indoor object detection and classification from scanned point clouds has recently attracted considerable research interest. However, detecting and classifying objects with arbitrary upward orientation has emerged as a substantial challenge. This paper presents an anchor-based graph method via geometric and topological similarity among indoor objects. With this method, the misclassification that usually occurs in the objects placed non-vertical with the floor is overcome by extracting anchor in each graph via nodes’ geometric attribute and by matching graph via topological relationshi

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Harkare, Aarya, and Dr Rupesh C. Jaiswal. "Object Fetching UAV using Autonomous Flight and Object Detection Algorithms." International Journal for Research in Applied Science and Engineering Technology 11, no. 9 (2023): 602–10. http://dx.doi.org/10.22214/ijraset.2023.55692.

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Abstract: Autonomous UAVs (Unmanned Aerial Vehicles) are being used in various applications around the world like surveillance and aerial security, construction, agriculture, delivery, etc. These applications require heavy duty UAVs with high payload capacity and long battery life. The aim of this project is to use the principles of autonomous flight and object detection for indoor autonomous flight with the target to fetch and deliver lightweight objects with minimum energy and time consumption to create a viable prototype for indoor applications like delivering objects to bedridden patients,

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Putri, Khalisha, and Ika Candradewi -. "Panoptic Segmentation for Indoor Environments using MaskDINO: An Experiment on the Impact of Contrast." ELCVIA Electronic Letters on Computer Vision and Image Analysis 24, no. 1 (2025): 1–29. https://doi.org/10.5565/rev/elcvia.1861.

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Robot perception involves recognizing the surrounding environment, particularly in indoor spaces like kitchens, classrooms, and dining areas. This recognition is crucial for tasks such as object identification. Objects in indoor environments can be categorized into "things," with fixed and countable shapes (e.g., tables, chairs), and "stuff," which lack a fixed shape and cannot be counted (e.g., sky, walls). Object detection and instance segmentation methods excel in identifying "things," with instance segmentation providing more detailed representations than object detection. However, semanti

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He, Yu, Chengpeng Jin, and Xuesong Zhang. "Enhancing Indoor Object Detection with xLSTM Attention-Driven YOLOv9 for Improved 2D-Driven 3D Object Detection." Journal of Electronic Research and Application 9, no. 2 (2025): 1–6. https://doi.org/10.26689/jera.v9i2.9698.

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Three-dimensional (3D) object detection is crucial for applications such as robotic control and autonomous driving. While high-precision sensors like LiDAR are expensive, RGB-D sensors (e.g., Kinect) offer a cost-effective alternative, especially for indoor environments. However, RGB-D sensors still face limitations in accuracy and depth perception. This paper proposes an enhanced method that integrates attention-driven YOLOv9 with xLSTM into the F-ConvNet framework. By improving the precision of 2D bounding boxes generated for 3D object detection, this method addresses issues in indoor enviro

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Shyamala, A., S. Selvaperumal, and G. Prabhakar. "Anfis Classifier Based Moving Object Detection and Segmentation in Indoor and Outdoor Environments." Current Signal Transduction Therapy 14, no. 1 (2019): 21–30. http://dx.doi.org/10.2174/1574362413666180226113024.

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Background: Moving object detection in dynamic environment video is more complex than the static environment videos. In this paper, moving objects in video sequences are detected and segmented using feature extraction based Adaptive Neuro-Fuzzy Inference System (ANFIS) classifier approach. The proposed moving object detection methodology is tested on different video sequences in both indoor and outdoor environments. Methods: This proposed methodology consists of background subtraction and classification modules. The absolute difference image is constructed in background subtraction module. The

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Hassan, Salam Hassan Jaleel Salma Hameedi. "You Only Look Once (YOLOv3): Object Detection and Recognition for Indoor Environment." Multicultural Education 7, no. 6 (2021): 171. https://doi.org/10.5281/zenodo.4906284.

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<em>Computer Vision (CV) is a study field that is responsible for developing techniques to perform tasks that the human visual system can do. Object detection is a technique used for detecting objects in videos and images. The research aims at detecting objects for indoor environment such as offices or rooms in different conditions of lighting by using YOLOv3 and generating a voice message for each detected object. YOLOv3 outperforms the other deep learning algorithms such as CNN because it looks at the entire image by predicting the bounding boxes using Convolutional Neural Network

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Kolodiazhnyi, Maksim, Anna Vorontsova, Matvey Skripkin, Danila Rukhovich, and Anton Konushin. "UniDet3D: Multi-dataset Indoor 3D Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 39, no. 4 (2025): 4365–73. https://doi.org/10.1609/aaai.v39i4.32459.

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Growing customer demand for smart solutions in robotics and augmented reality has attracted considerable attention to 3D object detection from point clouds. Yet, existing indoor datasets taken individually are too small and insufficiently diverse to train a powerful and general 3D object detection model. In the meantime, more general approaches utilizing foundation models are still inferior in quality to those based on supervised training for a specific task. In this work, we propose UniDet3D, a simple yet effective 3D object detection model, which is trained on a mixture of indoor datasets an

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Choi, Mi-Young, Gye-Young Kim, and Hyung-Il Choi. "Robust Object Detection from Indoor Environmental Factors." Journal of the Korea Society of Computer and Information 15, no. 2 (2010): 41–46. http://dx.doi.org/10.9708/jksci.2010.15.2.041.

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Ding, Xintao, Boquan Li, and Jinbao Wang. "Geometric property-based convolutional neural network for indoor object detection." International Journal of Advanced Robotic Systems 18, no. 1 (2021): 172988142199332. http://dx.doi.org/10.1177/1729881421993323.

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Indoor object detection is a very demanding and important task for robot applications. Object knowledge, such as two-dimensional (2D) shape and depth information, may be helpful for detection. In this article, we focus on region-based convolutional neural network (CNN) detector and propose a geometric property-based Faster R-CNN method (GP-Faster) for indoor object detection. GP-Faster incorporates geometric property in Faster R-CNN to improve the detection performance. In detail, we first use mesh grids that are the intersections of direct and inverse proportion functions to generate appropri

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20

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

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Jerome Daim, Terence, and Razak Mohd Ali Lee. "A weighted least squares consideration for IR-UWB radar based device-free object positioning estimation for indoor environment." Indonesian Journal of Electrical Engineering and Computer Science 15, no. 2 (2019): 894. http://dx.doi.org/10.11591/ijeecs.v15.i2.pp894-901.

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<p>Impulse Radio Ultra-Wideband (IR-UWB) radar is a type of radar functioning based on UWB transmission technology that uses an exceedingly wide bandwidth low power impulse signal to continuously transmitting and receiving the impulse signal for object detection within a range. To date, most of the proposed Ultra-Wideband (UWB) transmission technology based object positioning estimation systems for indoor environment depends on objects to be attached with an active UWB devices. In certain circumstances, it is ideal to track objects in passive manner without the requirement of any attache

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Batra, Aman, Fawad Sheikh, Maher Khaliel, Michael Wiemeler, Diana Göhringer, and Thomas Kaiser. "Object Recognition in High-Resolution Indoor THz SAR Mapped Environment." Sensors 22, no. 10 (2022): 3762. http://dx.doi.org/10.3390/s22103762.

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Synthetic aperture radar (SAR) at the terahertz (THz) spectrum has emerging short-range applications. In comparison to the microwave spectrum, the THz spectrum is limited in propagation range but benefits from high spatial resolution. The THz SAR is of significant interest for several applications which necessitate the mapping of indoor environments to support various endeavors such as rescue missions, map-assisted wireless communications, and household robotics. This paper addresses the augmentation of the high-resolution indoor mapped environment for object recognition, which includes detect

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Luo, Hao, Wenjie Luo, and Wenzhu Yang. "Camera Pose Generation Based on Unity3D." Information 16, no. 4 (2025): 315. https://doi.org/10.3390/info16040315.

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Deep learning models performing complex tasks require the support of datasets. With the advancement of virtual reality technology, the use of virtual datasets in deep learning models is becoming more and more widespread. Indoor scenes represents a significant area of interest for the application of machine vision technologies. Existing virtual indoor datasets exhibit deficiencies with regard to camera poses, resulting in problems such as occlusion, object omission, and objects having too small of a proportion of the image, and perform poorly in the training for object detection and simultaneou

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Mohammad, Hazem, Sameer Kishore, and Judhi Prasetyo. "Developing an Object Detection and Gripping Mechanism Algorithm using Machine Learning." Journal of Applied Science and Advanced Engineering 1, no. 2 (2023): 47–54. http://dx.doi.org/10.59097/jasae.v1i2.15.

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Localizing and recognition of objects are critical problems for indoor manipulation tasks. This paper describes an algorithm based on computer vision and machine learning that does object detection and gripping tasks. Detection of objects is carried out using a combination of a camera and depth sensor using a Kinect v1 depth sensor. Moreover, machine learning algorithms (YOLO) are used for computer vision. The project presents a method that allows the Kinect sensor to detect objects' 3D location. At the same time, it is attached to any robotic arm base, allowing for a more versatile and compac

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Dong, Shou Long, Xiang Guang Chen, Wang Yang Yu, and Yan Hua Yin. "Indoor Passive Millimeter-Wave Imaging for Concealed Object Detection." Advanced Materials Research 760-762 (September 2013): 1581–84. http://dx.doi.org/10.4028/www.scientific.net/amr.760-762.1581.

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Passive millimeter-wave (PMMW) imaging is an effective technique for concealed objects detection. A PMMW imaging system optimized for corresponding with indoor surroundings is developed and tested. The metal is deposited on the surface of carbon fiber by chemical plating process and the temperature contrast between the human body and concealed objects can be improved by adding the metal deposited carbon fiber to concealed objects. An image enhancement algorithm is proposed on the basis of the combination of wavelet transformation and top-hit transformation, and the experiment results demonstra

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Nenchoo and Tantrairatn. "Real-Time 3D UAV Pose Estimation by Visualization." Proceedings 39, no. 1 (2020): 18. http://dx.doi.org/10.3390/proceedings2019039018.

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This paper presents an estimation of 3D UAV position in real-time condition by using Intel RealSense Depth camera D435i with visual object detection technique as a local positioning system for indoor environment. Nowadays, global positioning system or GPS is able to specify UAV position for outdoor environment. However, for indoor environment GPS hasn’t a capability to determine UAV position. Therefore, Depth stereo camera D435i is proposed to observe on ground to specify UAV position for indoor environment instead of GPS. Using deep learning for object detection to identify target object with

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Borkivskyi, B. P., and V. M. Teslyuk. "OPTIMIZATION OF OBJECT DETECTION IN CLOSED SPACE USING MOBILE ROBOTIC SYSTEMS WITH OBSTACLE AVOIDANCE." Ukrainian Journal of Information Technology 6, no. 2 (2024): 41–48. https://doi.org/10.23939/ujit2024.02.041.

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Introducing neural network training process modification that uses combination of several datasets to optimize search of objects and obstacles using mobile robotic systems in a closed space. The study includes an analysis of papers and existing approaches aiming to solve the problem of object boundary detection and discovers the key features of several neural network architectures. During research, it was discovered that there is an insufficient amount of data about the effectiveness of using obstacle detection approaches by mobile robotics systems in a closed space. The presented method is a

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Mocanu, Irina, Georgiana Scarlat, Lucia Rusu, Ionut Pandelica, and Bogdan Cramariuc. "Indoor Localisation through Probabilistic Ontologies." International Journal of Computers Communications & Control 13, no. 6 (2018): 988–1006. http://dx.doi.org/10.15837/ijccc.2018.6.3022.

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For elderly people that are living alone in their homes there is a need to permanently monitor them. One of this aspect consist in knowing their indoor position and motion behavioural status, in real time. One possibility for indoor positioning of an user consists in understanding the images provided by supervising cameras. In this case the main aspect is represented by recognition of objects from these images. Thus, object recognition plays an essential part in understanding the environment and adding meaning to it. This paper presents a method for indoor localisation based on identifying the

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Rukhovich, D. D. "2D-to-3D Projection for Monocular and Multi-View 3D Multi-class Object Detection in Indoor Scenes." Programmnaya Ingeneria 12, no. 9 (2021): 459–69. http://dx.doi.org/10.17587/prin.12.459-469.

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In this paper, we propose a novel method of joint 3D object detection and room layout estimation. The proposed method surpasses all existing methods of 3D object detection from monocular images on the indoor SUN RGB-D dataset. Moreover, the proposed method shows competitive results on the ScanNet dataset in multi-view mode. Both these datasets are collected in various residential, administrative, educational and industrial spaces, and altogether they cover almost all possible use cases. Moreover, we are the first to formulate and solve a problem of multi-class 3D object detection from multi-vi

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Theodorou, Charalambos, Vladan Velisavljevic, and Vladimir Dyo. "Visual SLAM for Dynamic Environments Based on Object Detection and Optical Flow for Dynamic Object Removal." Sensors 22, no. 19 (2022): 7553. http://dx.doi.org/10.3390/s22197553.

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In dynamic indoor environments and for a Visual Simultaneous Localization and Mapping (vSLAM) system to operate, moving objects should be considered because they could affect the system’s visual odometer stability and its position estimation accuracy. vSLAM can use feature points or a sequence of images, as it is the only source of input that can perform localization while simultaneously creating a map of the environment. A vSLAM system based on ORB-SLAM3 and on YOLOR was proposed in this paper. The newly proposed system in combination with an object detection model (YOLOX) applied on extracte

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Chen, Ming-Chih, and Yang-Ming Liu. "An Indoor Video Surveillance System with Intelligent Fall Detection Capability." Mathematical Problems in Engineering 2013 (2013): 1–8. http://dx.doi.org/10.1155/2013/839124.

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This work presents a novel indoor video surveillance system, capable of detecting the falls of humans. The proposed system can detect and evaluate human posture as well. To evaluate human movements, the background model is developed using the codebook method, and the possible position of moving objects is extracted using the background and shadow eliminations method. Extracting a foreground image produces more noise and damage in this image. Additionally, the noise is eliminated using morphological and size filters and this damaged image is repaired. When the image object of a human is extract

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Sanboontho, Wuttipong, Auraluck Pichitkul, Suradet Tantrairatn, Tharathep Phiboon, and Atthaphon Ariyarit. "Indoor Localization and Measurement Object in Field for TurtleBot Using Combined 2D-LiDAR and Monocular Camera." Journal of Physics: Conference Series 3022, no. 1 (2025): 012004. https://doi.org/10.1088/1742-6596/3022/1/012004.

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Abstract Robotic technology has developed in various ways to challenge problems in complex and modern situations. Working in indoor spaces creates many extra complications due to intricacy in spaces and due to signal instability, that tends to make navigation efficiency difficult for mobile robots. Localizing objects correctly and Measurement them is fundamental. This was achieved using a SLAM system with 2D LiDAR to map the environment and added object detection from a monocular camera that identifies and tracks specific items within the view of the robot. It improves the view of the robot by

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Kuruvilla, Chinnu, Sony Thomas, and Preena Prasad. "Real Time Indoor Object Detection Aid for Blind." IARJSET 8, no. 6 (2021): 617–23. http://dx.doi.org/10.17148/iarjset.2021.86106.

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Pokuciński, Sebastian, and Dariusz Mrozek. "Object Detection with YOLOv5 in Indoor Equirectangular Panoramas." Procedia Computer Science 225 (2023): 2420–28. http://dx.doi.org/10.1016/j.procs.2023.10.233.

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Sardar, Santu, Amit K. Mishra, and M. Z. A. Khan. "LTE commsense for object detection in indoor environments." IEEE Aerospace and Electronic Systems Magazine 33, no. 7 (2018): 46–59. http://dx.doi.org/10.1109/maes.2017.180054.

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Hong, Jongkwang, Yongwon Hong, Youngjung Uh, and Hyeran Byun. "Discovering overlooked objects: Context-based boosting of object detection in indoor scenes." Pattern Recognition Letters 86 (January 2017): 56–61. http://dx.doi.org/10.1016/j.patrec.2016.12.017.

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Chen, Haotian, Chao Zhang, and Zhouchen Lin. "Video Surveillance for Indoor Office Environment Based on Object-Level Anomaly Detection." Journal of Physics: Conference Series 2504, no. 1 (2023): 012029. http://dx.doi.org/10.1088/1742-6596/2504/1/012029.

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Abstract Traditional methods of Abnormal Behavior Detection (ABD) process the surveillance video on frame-level, which ignores object-level abnormal behavior patterns. To address the problem, this paper presents Object-Level Anomaly Detection model (OLAD), which aims to model various normal behavior patterns of different objects and the normal interaction patterns between them. Specifically, OLAD introduces an encoding-embedding network to transform object-level information into the feature space. By integrating such information, OLAD processes both frame-level and object-level cues in the vid

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Li, Yiran, Han Xie, and Hyunchul Shin. "3D Object Detection Using Frustums and Attention Modules for Images and Point Clouds." Signals 2, no. 1 (2021): 98–107. http://dx.doi.org/10.3390/signals2010009.

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Three-dimensional (3D) object detection is essential in autonomous driving. Three-dimensional (3D) Lidar sensor can capture three-dimensional objects, such as vehicles, cycles, pedestrians, and other objects on the road. Although Lidar can generate point clouds in 3D space, it still lacks the fine resolution of 2D information. Therefore, Lidar and camera fusion has gradually become a practical method for 3D object detection. Previous strategies focused on the extraction of voxel points and the fusion of feature maps. However, the biggest challenge is in extracting enough edge information to de

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Lecrosnier, Louis, Redouane Khemmar, Nicolas Ragot, et al. "Deep Learning-Based Object Detection, Localisation and Tracking for Smart Wheelchair Healthcare Mobility." International Journal of Environmental Research and Public Health 18, no. 1 (2020): 91. http://dx.doi.org/10.3390/ijerph18010091.

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This paper deals with the development of an Advanced Driver Assistance System (ADAS) for a smart electric wheelchair in order to improve the autonomy of disabled people. Our use case, built from a formal clinical study, is based on the detection, depth estimation, localization and tracking of objects in wheelchair’s indoor environment, namely: door and door handles. The aim of this work is to provide a perception layer to the wheelchair, enabling this way the detection of these keypoints in its immediate surrounding, and constructing of a short lifespan semantic map. Firstly, we present an ada

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Yang, Kaichen, Tzungyu Tsai, Honggang Yu, Tsung-Yi Ho, and Yier Jin. "Beyond Digital Domain: Fooling Deep Learning Based Recognition System in Physical World." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (2020): 1088–95. http://dx.doi.org/10.1609/aaai.v34i01.5459.

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Adversarial examples that can fool deep neural network (DNN) models in computer vision present a growing threat. The current methods of launching adversarial attacks concentrate on attacking image classifiers by adding noise to digital inputs. The problem of attacking object detection models and adversarial attacks in physical world are rarely touched. Some prior works are proposed to launch physical adversarial attack against object detection models, but limited by certain aspects. In this paper, we propose a novel physical adversarial attack targeting object detection models. Instead of simp

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Zhu, Yun, Le Hui, Yaqi Shen, and Jin Xie. "SPGroup3D: Superpoint Grouping Network for Indoor 3D Object Detection." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 7 (2024): 7811–19. http://dx.doi.org/10.1609/aaai.v38i7.28616.

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Current 3D object detection methods for indoor scenes mainly follow the voting-and-grouping strategy to generate proposals. However, most methods utilize instance-agnostic groupings, such as ball query, leading to inconsistent semantic information and inaccurate regression of the proposals. To this end, we propose a novel superpoint grouping network for indoor anchor-free one-stage 3D object detection. Specifically, we first adopt an unsupervised manner to partition raw point clouds into superpoints, areas with semantic consistency and spatial similarity. Then, we design a geometry-aware votin

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Hamidon, Munirah Hayati, and Tofael Ahamed. "Detection of Tip-Burn Stress on Lettuce Grown in an Indoor Environment Using Deep Learning Algorithms." Sensors 22, no. 19 (2022): 7251. http://dx.doi.org/10.3390/s22197251.

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Lettuce grown in indoor farms under fully artificial light is susceptible to a physiological disorder known as tip-burn. A vital factor that controls plant growth in indoor farms is the ability to adjust the growing environment to promote faster crop growth. However, this rapid growth process exacerbates the tip-burn problem, especially for lettuce. This paper presents an automated detection of tip-burn lettuce grown indoors using a deep-learning algorithm based on a one-stage object detector. The tip-burn lettuce images were captured under various light and indoor background conditions (under

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Wang, Runzhi, Wenhui Wan, Yongkang Wang, and Kaichang Di. "A New RGB-D SLAM Method with Moving Object Detection for Dynamic Indoor Scenes." Remote Sensing 11, no. 10 (2019): 1143. http://dx.doi.org/10.3390/rs11101143.

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Simultaneous localization and mapping (SLAM) methods based on an RGB-D camera have been studied and used in robot navigation and perception. So far, most such SLAM methods have been applied to a static environment. However, these methods are incapable of avoiding the drift errors caused by moving objects such as pedestrians, which limits their practical performance in real-world applications. In this paper, a new RGB-D SLAM with moving object detection for dynamic indoor scenes is proposed. The proposed detection method for moving objects is based on mathematical models and geometric constrain

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Kasinath, S., S. K. Stephan, Edward Lisha, K.G. Parthive, and R. Remya. "Enhanced Blind Navigation using YOLO and Sensor Fusion." Recent Innovations in Wireless Network Security 7, no. 3 (2025): 1–10. https://doi.org/10.5281/zenodo.15516516.

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<em>Blind navigation remains a significant challenge for visually impaired individuals, particularly in complex and dynamic environments such as crowded streets, public transport, and indoor spaces. Traditional mobility aids like canes and guide dogs offer assistance but have limitations in detecting fast-moving obstacles or recognizing objects beyond immediate reach. With advancements in artificial intelligence (AI) and sensor technologies, there is an opportunity to develop smarter, real-time navigation solutions that enhance mobility and independence for visually impaired individuals.</em>

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Cong, Peichao, Junjie Liu, Jiaxing Li, et al. "YDD-SLAM: Indoor Dynamic Visual SLAM Fusing YOLOv5 with Depth Information." Sensors 23, no. 23 (2023): 9592. http://dx.doi.org/10.3390/s23239592.

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Simultaneous location and mapping (SLAM) technology is key in robot autonomous navigation. Most visual SLAM (VSLAM) algorithms for dynamic environments cannot achieve sufficient positioning accuracy and real-time performance simultaneously. When the dynamic object proportion is too high, the VSLAM algorithm will collapse. To solve the above problems, this paper proposes an indoor dynamic VSLAM algorithm called YDD-SLAM based on ORB-SLAM3, which introduces the YOLOv5 object detection algorithm and integrates deep information. Firstly, the objects detected by YOLOv5 are divided into eight subcat

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Kowalewski, S., A. L. Maurin, and J. C. Andersen. "Semantic Mapping and Object Detection for Indoor Mobile Robots." IOP Conference Series: Materials Science and Engineering 517 (April 26, 2019): 012012. http://dx.doi.org/10.1088/1757-899x/517/1/012012.

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Wang, Li, Ruifeng Li, Hezi Shi, et al. "Multi-Channel Convolutional Neural Network Based 3D Object Detection for Indoor Robot Environmental Perception." Sensors 19, no. 4 (2019): 893. http://dx.doi.org/10.3390/s19040893.

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Environmental perception is a vital feature for service robots when working in an indoor environment for a long time. The general 3D reconstruction is a low-level geometric information description that cannot convey semantics. In contrast, higher level perception similar to humans requires more abstract concepts, such as objects and scenes. Moreover, the 2D object detection based on images always fails to provide the actual position and size of an object, which is quite important for a robot’s operation. In this paper, we focus on the 3D object detection to regress the object’s category, 3D si

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Liao, Ziwei, Wei Wang, Xianyu Qi, and Xiaoyu Zhang. "RGB-D Object SLAM Using Quadrics for Indoor Environments." Sensors 20, no. 18 (2020): 5150. http://dx.doi.org/10.3390/s20185150.

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Indoor service robots need to build an object-centric semantic map to understand and execute human instructions. Conventional visual simultaneous localization and mapping (SLAM) systems build a map using geometric features such as points, lines, and planes as landmarks. However, they lack a semantic understanding of the environment. This paper proposes an object-level semantic SLAM algorithm based on RGB-D data, which uses a quadric surface as an object model to compactly represent the object’s position, orientation, and shape. This paper proposes and derives two types of RGB-D camera-quadric

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Vidya, M. Sri, and G. R. Sakthidharan. "Outlier Detection for IoT devices in Indoor Situating Framework using Machine Learning Techniques and Comparison." E3S Web of Conferences 309 (2021): 01024. http://dx.doi.org/10.1051/e3sconf/202130901024.

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Internet of Things connects various physical objects and form a network to do the services for sensing the physical things without any human intervention. They compute the data, retrieve the data by the network connections made through IoT device components such as Sensors, Protocols, Address, etc., The Global Positioning System (GPS) is used for localization in outer areas such as roads, and ground but cannot be used for Indoor environment. So, while using Indoor Environment, finding or locating an object is not possible by GPS. Therefore by using IoT devices such as Wi-Fi routers in Indoor E

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De Geyter, S., M. Bassier, H. De Winter, and M. Vergauwen. "OBJECT DETECTION AND LOCALISATION FOR BIM ENRICHMENT." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-1/W2-2023 (December 13, 2023): 155–62. http://dx.doi.org/10.5194/isprs-archives-xlviii-1-w2-2023-155-2023.

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Abstract. The use of Building Information Models (BIM) during the entire life cycle of a building or facility requires an up-to-date and detailed digital representation. Nowadays the BIM focus mostly lies on the design and construction phase of the building and is rarely used for the rest of the life cycle. For further use the BIM must be updated after construction and enriched with appliance objects such as safety equipment, heating elements to enhance its usability. In this work a new approach is presented to detect and locate appliance objects in a three dimensional environment using object

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