软件导刊2025，Vol.24Issue(3)：185-192,8.DOI:10.11907/rjdk.241986
一种改进YOLOv8s的太阳能电池板表面缺陷检测算法
A Solar Panel Defect Detection Algorithm Based on Improved YOLOv8s
付波 1廖和千1
作者信息
- 1. 湖北工业大学 电气与电子工程学院,湖北 武汉 430068
- 折叠
摘要
Abstract
Aiming at the problems of high missed detection rate and poor detection accuracy in traditional object detection methods for surface defect detection of solar panels,an improved YOLOv8s solar panel surface defect detection algorithm MCI-YOLOv8s is proposed.Firstly,multi-scale dilation attention is introduced into the YOLOv8s model to more effectively focus on the important feature information of defects at different scales in solar cells;Secondly,replacing the original detection head of YOLOv8s with a more lightweight distributed focus detection head can improve its feature extraction ability while reducing the number of model parameters;Finally,replace the original loss function with the Inner SIOU loss function to solve the problem of slow model convergence speed.The experiment shows that the mAP@0.5 of improved mod-el is 93.40%,which is 3.80%higher than the original benchmark model.The experimental results conducted on the NEU-DET dataset of steel strip surface defects confirm that the improved model has good generalization performance.Improving the speed and accuracy of model detec-tion meets industrial requirements,providing ideas for real-time detection of surface defects in solar panels.关键词
YOLOv8s/多尺度膨胀注意力/分布式焦点检测头/Inner-SIoU/太阳能电池板/缺陷检测Key words
YOLOv8s/multi-scale dilated attention/concentrated layerwise localization attention head/Inner-SIoU/solar panel/defect detection分类
信息技术与安全科学引用本文复制引用
付波,廖和千..一种改进YOLOv8s的太阳能电池板表面缺陷检测算法[J].软件导刊,2025,24(3):185-192,8.
