DRI-Net: a model for insulator defect detection on transmission lines in rainy backgrounds

Abstract

Transmission line insulators often operate in challenging weather conditions, particularly on rainy days. Continuous exposure to humidity and rain accelerates the aging process of insulators, leading to a decline in insulating material performance, the occurrence of cracks, and deformation. This situation poses a significant risk to the operation of the power system. Scene images collected on rainy days are frequently obstructed by rain lines, resulting in blurred backgrounds that significantly impact the performance of detection models. To improve the accuracy of insulator defect detection in rainy day environments, this paper proposes the DRI-Net (Derain-Insulator-net) detection model. Firstly, a dataset of insulator defects in rainy weather environments is constructed. Second, designing the de-raining model DRGAN and integrating it as an end-to-end DRGAN de-raining structural layer into the input end of the DRI-Net detection model, we significantly enhance the clarity and quality of images affected by rain, thereby reducing adverse effects such as image blurring and occlusion caused by rainwater. Finally, to enhance the lightweight performance of the model, partial convolution (PConv) and the lightweight upsampling operator CARAFE are utilized in the detection network to reduce the computational complexity of the model. The Wise-IoU bounding box regression loss function is applied to achieve faster convergence and improved detector accuracy. Experimental results demonstrate the effectiveness of the DRI-Net model in the task of rainy-day insulator defect detection, achieving an average precision MAP value of 82.65% in the established dataset. Additionally, an online detection system for rainy day insulator defects is designed in conjunction with the detection model, demonstrating practical engineering applications value.