학술논문
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
Document Type
Academic Journal
Author
蒋成晨; 何坚强; 陆群; 王江峰; 殷宇翔; 骆杨; JIANG Chengchen; HE Jianqiang; LU Qun; WANG Jiangfeng; YIN Yuxiang; LUO Yang
Source
计算机与数字工程 / Computer and Digital Engineering. 51(10):2277-2281
Subject
Language
Chinese
ISSN
1672-9722
Abstract
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 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.
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.