基于改进Faster R-CNN的变电站设备外部缺陷检测OA北大核心CSTPCD

There are challenges in object detection on external defects of transformer substation equipment,such as vari-ous target shapes,complex surrounding environment,low recognition accuracy of current representative algorithms,and severe false or missed detection.By comparing the detection results of different object detection algorithms on the trans-former substation equipment defect data set,it is revealed that the faster R-CNN algorithm with the feature fusion pyr-amid structure has higher detection accuracy.However,there are still opportunities to improve the detection accuracy of small target objects and equipment leakage.Thus,in this study,an enhanced,faster R-CNN-based algorithm is de-veloped.It improves the detection accuracy of defects by enhancing the input image data,adding the spatial pyramid pooling structure to the network to improve the feature fusion method,and thereby boosting the classification and bounding box regression loss function.Compared with the original faster R-CNN,the experimental findings demon-strate that the improved algorithm has increased AP(0.5:0.95)(average precision)by 2.7%and AP(0.5)by 4.3%in the detection results of the transformer substation equipment with respect to the defect object detection data set and the de-tection accuracy of small target objects has also been improved by 1.8%.This work confirms the effectiveness of the method proposed here.