计算机与现代化Issue(9)：43-49,54,8.DOI:10.3969/j.issn.1006-2475.2025.09.006
基于改进YOLOv8-EDD的光伏板缺陷检测
Defect Detection of Photovoltaic Panel Based on Improved YOLOv8-EDD
摘要
Abstract
To solve the problems of low accuracy and slow detection speed of existing defect detection methods for photovoltaic panels,an novel defect detection model for photovoltaic panels is proposed based on improved YOLOv8-EDD.Firstly,multi-scale attention mechanism EMA is introduced to enable YOLOv8 model to pay more attention to the defect area of photovoltaic panels.Secondly,deformable convolutional DCNv2 is embedded into the original C2f module to enhance the model's ability to extract irregular defect shape.At the same time,in order to alleviate the problem of reduced model detection speed due to the large number of DCNv2 parameters,the DySample lightweight upsampling operator is used to replace the original upsampling op-erator of YOLOv8 to reduce the number of model parameters and calculation complexity,thus to enhance the detecting speed.Fi-nally,WIoUv3 loss function is integrated to reduce the influence of low-quality samples on the accuracy and improve the general-ization ability of the model.In the experiment,compared with the original model,the accuracy of the improved YOLOv8-EDD model increases by 15.3 percentage points,the recall rate increases by 11.3 percentage points,mean of the average accuracy in-creases by 10.5 percentage points,and the detection speed has increased by 6.5 FPS.The results show that the proposed model not only improves the detection accuracy but also has faster detection speed,and is more suitable for the defect detection of pho-tovoltaic panels.关键词
YOLOv8/光伏板缺陷检测/EMA/DySample/WIoUv3Key words
YOLOv8/photovoltaic panel defect detection/EMA/DySample/WIoUv3分类
信息技术与安全科学引用本文复制引用
贾涛,吴月超,吕洋,付文龙..基于改进YOLOv8-EDD的光伏板缺陷检测[J].计算机与现代化,2025,(9):43-49,54,8.基金项目
湖北省自然科学基金联合基金资助项目(2024AFD409) （2024AFD409）
