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

作　　者：付波[1] 廖和千 FU Bo;LIAO Heqian(School of Electrical and Electronic Engineering,Hubei University of Technology,Wuhan 430068,China)

机构地区：[1]湖北工业大学电气与电子工程学院,湖北武汉430068

出　　处：《软件导刊》2025年第3期185-192,共8页Software Guide

摘　　要：针对传统目标检测方法在检测太阳能电池片表面缺陷时存在漏检率高、检测精度差等问题,提出一种改进YOLOv8s的太阳能电池板表面缺陷检测算法MCI-YOLOv8s。首先,在YOLOv8s模型中引入多尺度膨胀注意力,使其更加有效地关注太阳能电池片中不同尺度缺陷的重要特征信息;其次,将YOLOv8s原检测头替换为更加轻量化的分布式焦点检测头,在降低模型参数量的同时提高其对特征的提取能力;最后,将原有损失函数替换为Inner-SIoU损失函数,解决模型收敛速度较慢的问题。实验结果表明,改进模型的mAP@0.5为93.40%,较原基准模型提高了3.80%。在钢带表面缺陷数据集NEU-DET上进行的实验结果证实改进模型具有较好的泛化性能。改进模型检测速度与精度符合工业需求,为太阳能电池板表面缺陷的实时检测提供了思路。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 model 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 detection 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 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]