改进YOLOv5的光伏组件热斑及遮挡小目标检测OACSTPCD

Hot spots will seriously affect the power generation efficiency of photovoltaic modules,infrared image detec-tion of hot spots is difficult to realize effective recognition of small foreign matters such as leaves and bird droppings,discovering and cleaning foreign matters timely can effectively reduce the hot spots caused by continuous covering.In order to realize more comprehensive recognition and treatment of hot spots,based on the image size of UAV inspection visible and infrared video and the characteristics of detection task,YOLOv5's anchor frame setting scheme is improved by combining K-means++ algorithm and IoU index,the randomness of results has been improved.In the visible scene,aiming at the problem that small occluded objects make detection difficult,the small occluded objects detection model(CA-YOLOv5s6)is designed by embedding coordinate attention(CA)in YOLOv5s6's backbone.In the infrared scene,the hot spot area is obvious in infrared image,the lightweight network YOLOv5n is selected as its detection model.The experimental results show that,compared with YOLOv5s6,the mAP of CA-YOLOv5s6 is increased by 2.97 percentage points to 83.78%,and the Parameters are reduced by 4.8×105 to 1.18×107,which effectively improves the detection accuracy of the occlusion small target.The mAP,FPS and Parameters of YOLOv5n are 93.31%,83.3 and 1.76×106,which can better meet the task requirements of infrared image hot spot detection.