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Multi Camera Localization Handover Based on yolo Object Detection algorithm in Complex Environments

IEEE ACCESS 2024年 12卷 15236-15250页

作者： Wu, Wennan Lai, Jizhou Nanjing Univ Aeronaut & Astronaut Coll Automat Engn Nanjing 210000 Peoples R China Jiangsu Satellite Nav Testing Ctr Co Ltd Nanjing 210000 Peoples R China

With the development of computer vision, image processing, and other technologies, the management of smart cities has been enhanced, and intelligent visual detection and tracking technology has progressed. A single-camera monitoring system presents challenges, including limited observation range, unstable tracking, and difficulties in recognizing complex scene obstructions. To overcome these obstacles, a multi-camera monitoring system must be implemented. To enhance the accuracy of multiple cameras' positioning and recognition, while also increasing their efficiency in recognizing targets, this study employs a novel approach that combines spatial mapping based on position data and feature matching based on target objects. Firstly, in the overlapping area of multiple camera targets, a uniform spatial constraint method is used to map and match the target object. The color features of the target object are used for matching. Secondly, the You only look once (yolo) object detection algorithm is introduced to recognize targets within the overlapping area of the camera using homologous transformation. In this way, a multi camera positioning technology based on yolo object detection algorithm is designed. The test results show that the yolov5 algorithm has a maximum mAP accuracy of 97.2% on the test set. At a reasoning speed of 10 ms, the yolov5 algorithm has a maximum mAP accuracy of 51.6%. The average values of the classification loss function, target loss function, and GloU loss function of the yolov5 algorithm are 0.001, 0.01, and 0.015, respectively. The error probability of yolo within 10cm in the DukeMTMC re TD dataset remains above 96.5%. The error probability of yolo within 9.5cm in the OTB dataset remains above 95%. When the target object is blocked, the highest accuracy of the yolo positioning system is 0.74. The above results indicate that the multi camera localization technology based on yolo object detection algorithm can improve the accuracy of localization

关键词： Object detection multiple cameras location tracking yolo algorithm homomorphic transformation

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yolo Target Detection algorithm with Deformable Convolution Kernel 18

YOLO Target Detection Algorithm with Deformable Convolution ...

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IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Wang, Hui Zhang, Shuai Yu, Lijun Shi, Ce Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

ISBN: (纸本)9781665441018

In order to solve the problem of inaccurate detection of targets with large scale and shape changes and small targets by the yolo target detection algorithm, a yolo target detection algorithm with deformable convolution kernel is proposed. The algorithm adds deformable convolution to the network, and designs three progressive embedding schemes, so that the network can adaptively change the receptive field of feature points according to the shape of the target, thereby extracting features more effectively and improving detection accuracy;by adjusting the backbone network the structure reduces the calculation amount of the algorithm. Tested on the VOC data set, the results show that the algorithm can effectively improve the accuracy of target detection, and the detection speed meets the real-time requirements.

关键词： yolo algorithm Deformable Convolution Receptive Field Target Detection

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yolo Object Detection algorithm with Hybrid Atrous Convolutional Pyramid 19

YOLO Object Detection Algorithm with Hybrid Atrous Convoluti...

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19th IEEE International Conference on Mechatronics and Automation (IEEE ICMA)

作者： Wang, Hui Wang, Zhiqiang Yu, Lijun He, Xinting Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China

ISBN: (数字)9781665408530

ISBN: (纸本)9781665408530;9781665408523

Aiming at the problems of insufficient detection accuracy of small targets and insufficient receptive field of feature points in the field of target detection, a yolo target detection algorithm with hybrid atrous convolution pyramid is proposed. The algorithm firstly introduces atrous convolutions with different dilation rates into the feature pyramid network, and builds a hybrid receptive field module (HRFM), which enhances the ability to obtain global information by increasing the receptive field, and solves the problem of target occlusion;Secondly, design a pyramid network of atrous path aggregation, fusion of shallow feature information and high-level semantic information, improve the global detail information and representation ability of feature maps, and enhance the multiscale adaptability of the model. Three progressive schemes are designed for testing on the VOC dataset. The experimental results show that the algorithm can effectively solve the problem of target occlusion and improve the detection accuracy of small targets.

关键词： Atrous convolution Receptive Field yolo algorithm Feature Pyramid

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Underwater Object Detection Using Deep Learning Techniques 3

Underwater Object Detection Using Deep Learning Techniques

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3rd International Conference on Intelligent Systems, Advanced Computing, and Communication, ISACC 2025

作者： Samanta, Sayan Malik, Parveen Samanta, Pravin Kumar KIIT Deemed to be University School of Electronics Engineering Odisha Bhubeneswar India TIET Deemed to be University ECE Department Punjab Patiala India

ISBN: (纸本)9798331523893

Underwater image detection plays a vital role in various marine applications such as oceanography, underwater robotics, and environmental monitoring. The challenges of underwater imaging, such as low visibility, distortion, and noise due to water quality and lighting conditions, make effective detection techniques crucial. This paper proposes an underwater image detection approach using the yolov8 (You Only Look Once version 8) model, a state-of-the-art deep learning architecture known for its efficiency in real-time object detection tasks. yolov8's ability to balance high accuracy and computational efficiency is leveraged to detect various objects in underwater environments, addressing challenges like water turbidity and poor lighting. A dataset of underwater images with annotated objects is used to train the model, evaluating its accuracy, speed, and robustness to distortions. Experimental results demonstrate that yolov8 outperforms traditional methods in detecting underwater objects with mAP50 of 80% and faster inference times, making it a promising solution for real-time underwater image analysis in diverse applications. © 2025 IEEE.

关键词： Deep Learning Model Efficiency Object Detection RE-DETR Underwater Image Detection yolo algorithm yoloNAS yolov5 yolov8

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Integrated yolo and CNN algorithms for Evaluating Degree of Walkway Breakage

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KSCE JOURNAL OF CIVIL ENGINEERING 2022年第8期26卷 3570-3577页

作者： Choi, Min Je Ku, Dong Gyun Lee, Seung Jae Univ Seoul Dept Transportat Engn Seoul 02504 South Korea Univ Seoul Dept Smart Cities Seoul 02504 South Korea

The focus of policymaking in Korea has changed from vehicle-centric road environments to people-centric environments. As the importance of walking has increased, the construction of pedestrian paths and interest in pedestrian environments have also increased. However, problem recognition and resolution require considerable time in the event of a problem in a pedestrian path. People with reduced mobility tend to resist changes in roads that they use. Thus, damaged pedestrian paths and obstacles pose a considerable risk and economic loss to transportation. In this study, we aimed to minimize the time and cost required for the evaluation of pedestrian paths by developing an automatic system for determining damage using integrated You Only Look Once (yolo) and convolutional neural network (CNN) image deep learning algorithms. We constructed a model using image deep learning by dividing the steps into walkway breakage detection and score evaluation according to the degree of breakage. The accuracy of the model was determined to be 92%. In the future, the evaluation of pedestrian path damage is expected to be automated using images and videos, thereby reducing the time required for the detection and restoration of damage.

关键词： Walkway yolo algorithm Image deep learning Breakage detection Walking environment assessment Pedestrian safety Evaluation indicators

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yolo-Xweld: Efficiently Detecting Pipeline Welding Defects in X-Ray Images for Constrained Environments

YOLO-Xweld: Efficiently Detecting Pipeline Welding Defects i...

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IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

作者： Yang, Jun Fu, Bo Zeng, Jinquan Wu, Shengxi Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu Peoples R China Carnegie Mellon Univ Coll Engn Pittsburgh PA 15213 USA

ISBN: (纸本)9781728186719

X-ray inspection of pipeline welds brings benefits for the industrial quality and safety of production. However, noise and complex texture structure in X-ray images caused by issues such as poor contrast and blurry defects lead to extraordinary obstacles for lightweight and high-precision identification to satisfy almost 100% accuracy requirements, especially in constrained environments. This paper proposes a novel method, named yolo-Xweld, to detect pipeline welding defects for constrained application scenarios. yolo-Xweld is derived from yolo v3-tiny by adding an SPP module to the backbone network to enhance multi-scale feature extraction, and weakening large-scale network detection branch based on characteristics of pipeline weld X-ray images. Depthwise separable convolution is introduced to achieve a lightweight model and reduce the dependence on the high-performance hardware. Experiments show that the number of the model's parameters and floating-point operations have dropped by over 70%, and the model size has been reduced to 1/15 of the original size, while the test accuracy is close to 100%. This is of great significance in reducing the dependence on high-performance hardware and enhancing the convenience and operability during inspections in the constrained pipeline weld detection environment.

关键词： X-ray pipeline weld target detection yolo algorithm weld defect deep learning

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AnAssistive Model for Visually Impaired People using yolo and MTCNN 19

AnAssistive Model for Visually Impaired People using YOLO an...

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3rd International Conference on Cryptography, Security and Privacy (ICCSP) / 4th International Conference on Multimedia and Image Processing (ICMIP)

作者： FerdousiRahman IsratJahanRitun NafisaFarhin JiaUddin BRAC Univ Dept Comp Sci & Engn 66 Mohakhali Dhaka Bangladesh

ISBN: (纸本)9781450366182

Visually impaired people face difficulties in safe and independent movement which deprive them from regular professional and social activities in both indoors and outdoors. Similarly they have distressin identification of surrounding environment fundamentals. This paper presents a model to detect brightness and major colors in real-time image by using RGB method by means of an external camera and then identification of fundamental objects as well as facial recognition from personal dataset. For the Object identification and Facial Recognition, yolo algorithm and MTCNN Networking are used, respectively. The software support is achieved by using OpenCV libraries of Python as well as implementing machine learning process. The major processor used for our model, Raspberry Pi scans and detects the facial edges via Pi camera and objects in the image are captured and recognized using mobile camera. Image recognition results are transferred to the blind users by means of text-to-speech library. The device portability is achieved by using a battery. The object detection process achieved 6-7 FPS processing with an accuracy rate of 63-80%. The face identification process achieved 80-100% accuracy.

关键词： Visually Impaired OpenCV Object Identification yolo algorithm Deep Learning

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Custom Face Recognition Using yolo.V3 3

Custom Face Recognition Using YOLO.V3

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3rd International Conference on Signal Processing and Communication (ICPSC)

作者： Menon, Suman M. Geroge, Anju Aswathy, N. James, Jaimy Adi Shankara Inst Engn & Technol VLSI & Embedded Syst Kalady India

ISBN: (纸本)9781665428644

Face recognition technique is adopted in many applications such as surveillance systems, medical field, security, robot navigation, etc. Today because of deep learning there is a large improvement in the area of face recognition. The importance of facial recognition comes to more importance at the time of a pandemic situation, for attendance marking people monitoring, in health care, social distancing etc. Many facial recognition algorithms are available today. But the performance of these algorithms lacks many factors and which lead to poor performance in real-time applications. The most commonly used algorithms for face recognition are R-CNN, Fast R-CNN, etc. The main problems with these algorithms are they have poor processing speed;they take time to produce the output. To overcome this problem, yolo.V3 algorithm can be used for facial recognition which produces faster output. In this paper we will study on different versions of yolo algorithms, study on existing algorithm for face detection and recognition (RCNN),and conclude with the result obtained for the yolo.V3 algorithm for recognizing a custom face.

关键词： Face recognition processing speed yolo algorithm R-CNN

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Smart Waste Manager: yolo-driven Medical Waste Detection and Classification System 1

Smart Waste Manager: YOLO-driven Medical Waste Detection and...

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1st IEEE International Conference on Smart Power Control and Renewable Energy, ICSPCRE 2024

作者： Bobe, Srushti Adhav, Priyanka Bhalerao, Omkar Chaware, Sandeep JSPM's Rajarshi Shahu College of Engineering Department of Information Technology Pune India

ISBN: (纸本)9798350377002

In the realm of healthcare waste management, the improper handling of medical waste poses significant threats to public health and the environment. The reliance on manual sorting procedures not only proves ineffective but also introduces risks for waste handlers and garbage collectors. To address these challenges and improve safety, efficiency, and ecological sustainability, this study proposes an innovative solution. We introduce a dedicated software system leveraging the yolo (You Only Look Once) algorithm for precise detection and classification of medical waste items. The envisioned system, while foregoing specific accuracy metrics, promises potential cost savings and plays a pivotal role in mitigating the hazards associated with manual sorting. This research advances a software solution for efficient medical waste detection and classification, bridging gaps in existing literature regarding accuracy issues, limited datasets, and the need for real-time detection capabilities, contributing to a safer and more sustainable waste management landscape. © 2024 IEEE.

关键词： Classification Deep learning Detection Garbage collectors Medical waste Management Safety yolo algorithm

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Improved small foreign object debris detection network based on yolov5

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JOURNAL OF REAL-TIME IMAGE PROCESSING 2024年第1期21卷 21-21页

作者： Zhang, Heng Fu, Wei Li, Dong Wang, Xiaoming Xu, Tengda Xihua Univ Sch Comp & Software Engn Chengdu Peoples R China Chengdu Univ Coll Chinese & ASEAN Arts Chengdu Peoples R China

In response to the challenges of detecting foreign object debris (FOD) on airport runways, where the objects are small in size and have indistinct features leading to false detections and missed detections, significant improvements were made to the yolov5 algorithm. First, the original yolov5-n model was optimized by incorporating multi-scale fusion and detection enhancements. To improve detection speed and reduce parameters, the detection head for large objects was removed. Second, the C3 module in the backbone network was replaced with the C2f module, resulting in enhanced gradient flow and improved feature representation capabilities. Additionally, the spatial pyramid pooling-fast (SPPF) module in the backbone network was refined to expand the receptive field and enhance the model's perception of dependencies between targets and backgrounds. Furthermore, the coordinate attention (CA) mechanism was introduced in the neck layer to further enhance the model's perception of small FOD items. Lastly, the SCYLLA-IoU (SIoU) loss function was introduced to further improve the speed and accuracy of bounding box regression. Moreover, the nearest neighbor interpolation upsampling method was substituted with the lightweight Content-Aware ReAssembly of FEatures (CARAFE) upsampling operator to better exploit global information. Experimental results on the Fod_Tiny dataset, which consists of small FOD items in airports, demonstrated a significant 5.4% improvement over the baseline algorithm. To validate the generalizability of the algorithm, experiments were conducted on the Mirco_COCO dataset, resulting in a notable 1.9% improvement compared to the baseline algorithm.

关键词： FOD Object detection yolo algorithm Deep learning

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