Visual clustering network-based intelligent power lines inspection system

Abstract

The huge differences in the visual shapes of multi-angle shooting objects leads to the poor performance of deep neural network (DNN). In this paper, an object detection model named TRUST-TECH-based visual clustering network (TTVCNet) for power line inspection is constructed. First, a TRUST-TECH-based visual clustering method (TTVCM) for multi-view-shape unsupervised clustering is proposed and can learn the difference in visual shape according to the object's views, which is the core of TTVCNet. Then, a Cascaded R–CNN object detection model based on TTVCNet is constructed for the power line inspection. Moreover, we apply the bilinear interpolation method and feature enhancement fusion techniques to this object detection model to solve the problem of small sample detection and semantic loss. In this paper, TTVCNet is applied to the MS-COCO 2017 dataset, and the test accuracy is improved up to 65.3%, especially the recognition accuracy of multi-view-shape is greatly improved. In the contrast experiment of self-made power line inspection dataset, the recognition accuracy of TTVCNet has been greatly improved, and the overall recognition accuracy is 86.3%.