计算机与数字工程2023，Vol.51Issue(10)：2277-2281,5.DOI:10.3969/j.issn.1672-9722.2023.10.012
一种改进YOLOv5算法的光伏热斑检测方法
A Method for Improving the YOLOv5 Algorithm for Photovoltaic Hot Spot Detection
摘要
Abstract
A method for improving the detection of hot spots on photovoltaic(PV)strings using an enhanced YOLOv5 algo-rithm is proposed.The presence of hot spots can lead to damage in PV string arrays.To enhance the recognition capability of un-manned aerial vehicle(UAV)inspection systems for hot spots on PV strings,the YOLOv5 algorithm is refined to improve the accu-racy and efficiency of hot spot detection.The improvement is achieved through the use of Puzzle Mix for data augmentation,which fo-cuses on small targets in the dataset image enhancement model.Additionally,a 3D non-local SimAM module is introduced into the Backbone to enhance the weight of hot spots in feature extraction,suppressing background interference weight.The CIoU(Complete Intersection over Union)loss function is employed to obtain a more precise training model and achieve high-precision localization.The enhanced algorithm is compared with other algorithms through experiments conducted on a self-made hot spot dataset.The re-sults indicate that the proposed method enhances the detection capability of hot spots on PV strings.This approach can serve as a technical reference for the inspection of PV power stations.关键词
YOLOv5/热斑/卷积神经网络/目标检测Key words
YOLOv5/hot spot/convolution neural network/object detection分类
信息技术与安全科学引用本文复制引用
蒋成晨,何坚强,陆群,王江峰,殷宇翔,骆杨..一种改进YOLOv5算法的光伏热斑检测方法[J].计算机与数字工程,2023,51(10):2277-2281,5.基金项目
国家自然科学基金,青年科学基金项目(编号:62003292)资助. （编号:62003292）
