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

热斑的存在会对光伏组串造成损坏,为了提高无人机巡检系统对光伏组串上热斑的识别能力,改进YOLOv5算法来提高对光伏组串上的热斑检测精度和效率。改进主要通过使用Puzzle Mix处理数据集图像增强模型的对小目标的关注,在Backbone引入3D无参SimAM模块来加强热斑在提取特征中的权重并抑制背景干扰权重,并使用CIOU损失函数来获得更精确的训练模型和高精度定位。将改进后的算法在自制热斑数据集上与其他算法进行对比实验,实验结果表明改进后的方对光伏组串热斑的检测能力增强。该方法可以为光伏电站的巡检提供技术参考。

A method for improving the detection of hot spots on photovoltaic(PV)strings using an enhanced YOLOv5 algorithm is proposed.The presence of hot spots can lead to damage in PV string arrays.To enhance the recognition capability of unmanned aerial vehicle(UAV)inspection systems for hot spots on PV strings,the YOLOv5 algorithm is refined to improve the accuracy and efficiency of hot spot detection.The improvement is achieved through the use of Puzzle Mix for data augmentation,which focuses 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 results 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.