Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental Learning
Defect detection constitutes one of the most crucial processes in industrial production. With a continuous increase in the number of defect categories and samples, the defect detection model underpinned by deep learning finds it challenging to expand to new categories, and the accuracy and real-time...
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MDPI AG
2024-09-01
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| Online Access: | https://www.mdpi.com/1424-8220/24/18/5921 |
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| author | Zhenwu Lei Yue Zhang Jing Wang Meng Zhou |
| author_facet | Zhenwu Lei Yue Zhang Jing Wang Meng Zhou |
| author_sort | Zhenwu Lei |
| collection | DOAJ |
| description | Defect detection constitutes one of the most crucial processes in industrial production. With a continuous increase in the number of defect categories and samples, the defect detection model underpinned by deep learning finds it challenging to expand to new categories, and the accuracy and real-time performance of product defect detection are also confronted with severe challenges. This paper addresses the problem of insufficient detection accuracy of existing lightweight models on resource-constrained edge devices by presenting a new lightweight YoloV5 model, which integrates four modules, SCDown, GhostConv, RepNCSPELAN4, and ScalSeq. Here, this paper abbreviates it as SGRS-YoloV5n. Through the incorporation of these modules, the model notably enhances feature extraction and computational efficiency while reducing the model size and computational load, making it more conducive for deployment on edge devices. Furthermore, a cloud-edge collaborative defect detection system is constructed to improve detection accuracy and efficiency through initial detection by edge devices, followed by additional inspection by cloud servers. An incremental learning mechanism is also introduced, enabling the model to adapt promptly to new defect categories and update its parameters accordingly. Experimental results reveal that the SGRS-YoloV5n model exhibits superior detection accuracy and real-time performance, validating its value and stability for deployment in resource-constrained environments. This system presents a novel solution for achieving efficient and accurate real-time defect detection. |
| format | Article |
| id | doaj-art-f9e689a5c2c64cfbbaa6928f4c05e2b3 |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-f9e689a5c2c64cfbbaa6928f4c05e2b32025-08-20T01:55:50ZengMDPI AGSensors1424-82202024-09-012418592110.3390/s24185921Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental LearningZhenwu Lei0Yue Zhang1Jing Wang2Meng Zhou3The School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, ChinaThe School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, ChinaThe School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, ChinaThe School of Electrical and Control Engineering, North China University of Technology, Beijing 100144, ChinaDefect detection constitutes one of the most crucial processes in industrial production. With a continuous increase in the number of defect categories and samples, the defect detection model underpinned by deep learning finds it challenging to expand to new categories, and the accuracy and real-time performance of product defect detection are also confronted with severe challenges. This paper addresses the problem of insufficient detection accuracy of existing lightweight models on resource-constrained edge devices by presenting a new lightweight YoloV5 model, which integrates four modules, SCDown, GhostConv, RepNCSPELAN4, and ScalSeq. Here, this paper abbreviates it as SGRS-YoloV5n. Through the incorporation of these modules, the model notably enhances feature extraction and computational efficiency while reducing the model size and computational load, making it more conducive for deployment on edge devices. Furthermore, a cloud-edge collaborative defect detection system is constructed to improve detection accuracy and efficiency through initial detection by edge devices, followed by additional inspection by cloud servers. An incremental learning mechanism is also introduced, enabling the model to adapt promptly to new defect categories and update its parameters accordingly. Experimental results reveal that the SGRS-YoloV5n model exhibits superior detection accuracy and real-time performance, validating its value and stability for deployment in resource-constrained environments. This system presents a novel solution for achieving efficient and accurate real-time defect detection.https://www.mdpi.com/1424-8220/24/18/5921cloud-edge collaborationlightweight YoloV5incremental learningdefect detectionelectronics manufacturing |
| spellingShingle | Zhenwu Lei Yue Zhang Jing Wang Meng Zhou Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental Learning Sensors cloud-edge collaboration lightweight YoloV5 incremental learning defect detection electronics manufacturing |
| title | Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental Learning |
| title_full | Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental Learning |
| title_fullStr | Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental Learning |
| title_full_unstemmed | Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental Learning |
| title_short | Cloud-Edge Collaborative Defect Detection Based on Efficient Yolo Networks and Incremental Learning |
| title_sort | cloud edge collaborative defect detection based on efficient yolo networks and incremental learning |
| topic | cloud-edge collaboration lightweight YoloV5 incremental learning defect detection electronics manufacturing |
| url | https://www.mdpi.com/1424-8220/24/18/5921 |
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