CPF Detector for Object Detection of Transmission Line External Force Damage

Abstract

It is significant to accurately identify the risk target of external force damage to ensure the safe operation of transmission lines. The recent object detection networks have provided ideas for determining the external force destruction risk object, and they have all fused deep and shallow features to a certain extent in the network. However, the fusion of features with different depths without processing will cause the current model to lose risk targets in weak areas. In this article, we propose a new cross-phase fusion (CPF) detector to detect small and weak risk targets effectively, which embeds a cross-fusion module of graph reasoning based on stripe pooling (CGRS) in specific layers. Specifically, CGRS adaptively captures random spatial dependence and long-range channel dependence with directional information to enhance the extraction ability of spatial features. On the other hand, CGRS utilizes an interactive projection mechanism to project spatial and semantic information into the graph space effectively. The reasoning between graph nodes realizes the dynamic correlation of target-related features and improves the feature representation of small targets. We conducted comprehensive experiments to prove that the value of mAP0.5 reached 80.1% in training external risk datasets for the CPF detector, which is superior to the current advanced methods and verifies its effectiveness for detecting external force damage risk. We believe that this method can have practical application value in maintaining the safety of transmission lines.