Graph-MDETR: A Graph-Guided Mamba-DETR Network for UAV Catenary Support Components Detection in Electrified Railways

As an important component of the catenary system in electrified railways, the operating status of the catenary support components (CSC) is related to the operational safety of the railway. They are usually detected by a camera on the inspection equipment using computer vision techniques. However, current CSC detection research faces the following challenges. 1) The components in the captured images have issues of multi-scale and multiple categories; 2) Some components are covered because of the limited shooting angle; 3) The existing detectors based on CNN and Transformer are difficult to associate features between long-interval regions. To solve these problems, a novel graph-guided Mamba-DETR detector (Graph-MDETR) is proposed for detecting CSC. Firstly, a new backbone network (DAMamba-Adapter) is proposed to enhance the model’s ability to associate features between long-interval regions, and multi-scale training (MST) is used for solving multi-scale problems. Secondly, the semantic graph-guided module (SGM) and positional graph-guided module (PGM) are designed to alleviate the problems of occlusion and multi-category components by extracting prior category relationships with global semantic features and component geometric information. Finally, a CSC dataset taken by a UAV is established, and various experiments are conducted to verify the effectiveness and superiority of the proposed method.