Abstract: Abnormal object detection of transmission lines is an important part of power system monitoring. However, the existing detection methods are not designed effectively for transmission line scenes. There are some problems such as insufficient features extracted by the depth network and lack of robustness under the influence of variable target environment and scale changes. This paper proposes an abnormal object detection method for electric transmission line, using HRNet as the backbone network to extract high-resolution features, combined with HRFPN to optimizing the quality of object feature representation, and balancing the proportion of positive and negative anchor count generated in the RPN, using cascaded object detectors for classification and bounding box regression. The test results in the transmission line scene show that the proposed detection method has higher detection performance, which performs better than Faster R-CNN and Cascade R-CNN.

Key words: electric transmission line, object detection, deep learning, HRNet