基于改进YOLOv8n的光伏板缺陷检测技术

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关键 词：光伏板缺陷检测;YOLOv8n;SCConv;CoordAtt;ADown 中图分类号：TP391.4 文献标识码：A doi：10.37188/CJLCD.2025-0166 CSTR：32172.14.CJLCD.2025-0166

Abstract： As a core component of solar power generation systems，defects on the surface of photovoltaic panels can seriously afect their photovoltaic conversion efficiency and service life. In response to the challenges of identifying smalldefects and low contrast between defects and background in photovoltaic panel defect detection，this study proposes the SCA-YOLOv8n detection model. First，the SCConv cross-coupling module was designed to enhance the model’s ability to extract multi-scale defect features while reducing redundant information through space-channel feature interactive reconstruction. Second， we construct the coordinate attention（CoordAtt） mechanism to focus on defect regions from the channel and spatial dimensions and suppress background interference.Finaly，a lightweight adaptive downsampling （ADown） module is embedded to replace traditional stride convolution， reducing computational complexity while minimizing feature information loss The experimental results show that the improved model achieves an mAP@0.5 of 94.4% ，which is a 2.0% improvement over the original YOLOv8n model. Additionally，the number of parameters is reduced by 5.0% ，and GFLOPs decrease by 4.9% These results comprehensively demonstrate that the proposed improvements not only achieve model lightweighting but also significantly enhance the accuracy and reliability of photovoltaic panel defect detection.

Key words： photovoltaic panel defect detection; YOLOv8n;SCConv; CoordAtt； ADown

1引言

在全球能源结构加速向低碳化转型的背景下，太阳能光伏发电作为可再生能源开发利用的关键技术[1-3]，其装机容量正以迅猛态势持续增长。（剩余13033字）