End-to-End Insulator String Defect Detection in a Complex Background Based on a Deep Learning Model - Xu, Weifeng; Zhong, Xiaohong; Luo, Man; Weng, Liguo; Zhou, Guohua (Frontiers in Energy Research, 2022)
Normal power line insulators ensure the safe transmission of electricity. The defects of insulator reduce the insulation, which may lead to failure of power transmission systems. As unmanned aerial vehicles (UAV) have developed rapidly, it is possible for workers to take and upload aerial images of insulator. Proposing a technology to detect insulator defect with high accuracy in short time can be of great value. The existing methods suffer from the complex backgrounds, so that they have to locate and extract the insulators at first. Some of them make detection relative to some specific conditions such as angle, brightness and object scale. This paper aims to make end-to-end detections using aerial images of insulator, giving the locations of insulator and defect at the same time while overcoming the disadvantages mentioned above. DEtection TRansformer (DETR) having encoder-decoder architecture adopts CNN as backbone network, applies self-attention mechanism for computing, and utilizes object queries instead of hand-crafted process to give the direct predictions. We modified this for insulator detection in complex aerial images. Based on the dataset we constructed, our model can get 97.97 in mean average precision (mAP) when setting the threshold of intersection over union (IoU) at 0.5 that is better than Cascade R-CNN and YOLOv5. The inference speed of our model can reach 25 frames per second (FPS) that is qualified for actual use. Experimental results demonstrate that our model meets the robustness and accuracy requirements for insulator defect detection.