Infrared Image Fault Diagnosis of Photovoltaic Modules Based on FMF-YOLOv5
Aiming at the problems of low contrast of infrared image and not obvious fault characteristics,the paper firstly proposes a new fusion attention mechanism(FAM)to focus on important fault characteristics.Secondly,a new fusion spa-tial pyramid pooling(FSPP)is created to enhance feature extraction capabilities.Finally,a new multi-level fusion convolu-tion(MFConv)is introduced,and a multi-level cross stage partial network is built by using MFConv.The MCSP module replaces the CSP module to maintain accuracy while increasing the number of model parameters with a small amount.The experimental results show that the average accuracy(mAP)of the proposed method reaches 93.1%when the IoU thresh-old is 0.5.This method provides a reliable and efficient fault detection solution for photovoltaic systems,making it a prac-tical solution to improve system performance and reduce maintenance costs.
万方数据
维普
