液晶与显示2025，Vol.40Issue(11)：1700-1709,10.DOI:10.37188/CJLCD.2025-0166
基于改进YOLOv8n的光伏板缺陷检测技术
Photovoltaic panel defect detection technology based on improved YOLOv8n
摘要
Abstract
As a core component of solar power generation systems,defects on the surface of photovoltaic panels can seriously affect their photovoltaic conversion efficiency and service life.In response to the challenges of identifying small defects 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.Finally,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.关键词
光伏板缺陷检测/YOLOv8n/SCConv/CoordAtt/ADownKey words
photovoltaic panel defect detection/YOLOv8n/SCConv/CoordAtt/ADown分类
计算机与自动化引用本文复制引用
邓万宇,袁昭阳..基于改进YOLOv8n的光伏板缺陷检测技术[J].液晶与显示,2025,40(11):1700-1709,10.基金项目
陕西省教育厅服务地方专项(No.19JC036) （No.19JC036）
陕西省"科学家+工程师"队伍项目(No.2023KXJ-091) Supported by Service Local Special Project of Shaanxi Provincial Department of Education(No.19JC036) （No.2023KXJ-091）
"Scientist&Engineer"Team Project of Shaanxi Province(No.2023KXJ-091) （No.2023KXJ-091）
