Research on Defect Detection in Lightweight Photovoltaic Cells Using YOLOv8-FSD
Given the high computational complexity and poor real-time performance of current photovoltaic cell surface defect detection methods, this study proposes a lightweight model, YOLOv8-FSD, based on YOLOv8. By introducing the FasterNet network to replace the original backbone network, computational com...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/3/843 |
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| author | Chao Chen Zhuo Chen Hao Li Yawen Wang Guangzhou Lei Lingling Wu |
| author_facet | Chao Chen Zhuo Chen Hao Li Yawen Wang Guangzhou Lei Lingling Wu |
| author_sort | Chao Chen |
| collection | DOAJ |
| description | Given the high computational complexity and poor real-time performance of current photovoltaic cell surface defect detection methods, this study proposes a lightweight model, YOLOv8-FSD, based on YOLOv8. By introducing the FasterNet network to replace the original backbone network, computational complexity and memory access are reduced. A thin neck structure designed based on hybrid convolution technology is adopted to reduce model parameters and computational load further. A lightweight dynamic feature upsampling operator improves the feature map quality. Additionally, the regularized Gaussian distribution distance loss function is used to enhance the detection ability for small target defects. Experimental results show that the YOLOv8-FSD lightweight algorithm improves detection accuracy while significantly reducing the number of parameters and computational requirements compared to the original algorithm. This improvement provides an efficient, accurate, and lightweight solution for PV cell defect detection. |
| format | Article |
| id | doaj-art-4e6e185e54a34998a2db0245ef770eba |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-4e6e185e54a34998a2db0245ef770eba2025-08-20T02:12:33ZengMDPI AGSensors1424-82202025-01-0125384310.3390/s25030843Research on Defect Detection in Lightweight Photovoltaic Cells Using YOLOv8-FSDChao Chen0Zhuo Chen1Hao Li2Yawen Wang3Guangzhou Lei4Lingling Wu5School of Computer Science and Engineering, Sichuan University of Science & Engineering, Zigong 643000, ChinaSchool of Computer Science and Engineering, Sichuan University of Science & Engineering, Zigong 643000, ChinaSchool of Computer Science and Engineering, Sichuan University of Science & Engineering, Zigong 643000, ChinaSchool of Computer Science and Engineering, Sichuan University of Science & Engineering, Zigong 643000, ChinaSchool of Computer Science and Engineering, Sichuan University of Science & Engineering, Zigong 643000, ChinaSchool of Computer Science and Engineering, Sichuan University of Science & Engineering, Zigong 643000, ChinaGiven the high computational complexity and poor real-time performance of current photovoltaic cell surface defect detection methods, this study proposes a lightweight model, YOLOv8-FSD, based on YOLOv8. By introducing the FasterNet network to replace the original backbone network, computational complexity and memory access are reduced. A thin neck structure designed based on hybrid convolution technology is adopted to reduce model parameters and computational load further. A lightweight dynamic feature upsampling operator improves the feature map quality. Additionally, the regularized Gaussian distribution distance loss function is used to enhance the detection ability for small target defects. Experimental results show that the YOLOv8-FSD lightweight algorithm improves detection accuracy while significantly reducing the number of parameters and computational requirements compared to the original algorithm. This improvement provides an efficient, accurate, and lightweight solution for PV cell defect detection.https://www.mdpi.com/1424-8220/25/3/843photovoltaic celldefect detectionYOLOv8lightweightingFasterNet |
| spellingShingle | Chao Chen Zhuo Chen Hao Li Yawen Wang Guangzhou Lei Lingling Wu Research on Defect Detection in Lightweight Photovoltaic Cells Using YOLOv8-FSD Sensors photovoltaic cell defect detection YOLOv8 lightweighting FasterNet |
| title | Research on Defect Detection in Lightweight Photovoltaic Cells Using YOLOv8-FSD |
| title_full | Research on Defect Detection in Lightweight Photovoltaic Cells Using YOLOv8-FSD |
| title_fullStr | Research on Defect Detection in Lightweight Photovoltaic Cells Using YOLOv8-FSD |
| title_full_unstemmed | Research on Defect Detection in Lightweight Photovoltaic Cells Using YOLOv8-FSD |
| title_short | Research on Defect Detection in Lightweight Photovoltaic Cells Using YOLOv8-FSD |
| title_sort | research on defect detection in lightweight photovoltaic cells using yolov8 fsd |
| topic | photovoltaic cell defect detection YOLOv8 lightweighting FasterNet |
| url | https://www.mdpi.com/1424-8220/25/3/843 |
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