Improved photovoltaic cell defect detection for YOLOv8

Home > Archive>Volume 48, Issue 1, 2025 >92-99

Improved photovoltaic cell defect detection for YOLOv8
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                        Yang LiYang Li
1.School of Automation and Electrical Engineering, Tianjin University of Technology and Education，Tianjin 300222, China； 2.Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education，Tianjin 300222, China
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Yang Chenchen1Yang Chenchen
School of Automation and Electrical Engineering, Tianjin University of Technology and Education，Tianjin 300222, China
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Yang GenghuangYang Genghuang
1.School of Automation and Electrical Engineering, Tianjin University of Technology and Education，Tianjin 300222, China； 2.Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education，Tianjin 300222, China
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Duan HailongDuan Hailong
1.School of Automation and Electrical Engineering, Tianjin University of Technology and Education，Tianjin 300222, China； 2.Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education，Tianjin 300222, China
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Deng Jingwei1Deng Jingwei
School of Automation and Electrical Engineering, Tianjin University of Technology and Education，Tianjin 300222, China
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Affiliation:1.School of Automation and Electrical Engineering, Tianjin University of Technology and Education，Tianjin 300222, China; 2.Tianjin Key Laboratory of Information Sensing and Intelligent Control, Tianjin University of Technology and Education，Tianjin 300222, China
Clc Number:TN41
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Abstract:

Aiming at the problems of false detection and missing detection in the complex background of photovoltaic cell defect detection, an improved YOLOv8 based photovoltaic cell defect detection algorithm was proposed. Firstly, the bidirectional feature pyramid network is used as the feature fusion mechanism to achieve multi-scale feature fusion through top-down and top-down paths. Secondly, the context aggregation module is introduced into the neck network, and the context information of different receptive fields is obtained by using the cavity convolution of different cavity convolution rates, which helps the model to identify small targets more accurately, and thus improves the target detection performance of the model. Finally, the boundary frame loss function is optimized and its weight factor is adjusted continuously to improve the convergence speed and efficiency of the model. The experimental results show that compared with the detection network of YOLOv8 algorithm, the recall rate and average accuracy are respectively increased by 10.4% and 1.8%, and the detection frame rate reaches 270 fps, ensuring the lightweight requirements of real-time detection and subsequent deployment. The improved algorithm can carry out robust detection of photovoltaic cell defects under complex background.

Key words:photovoltaic cells;defect detection;YOLOv8;attention mechanism;loss function

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Online: February 24,2025
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