Catenary support components (CSCs) are essential for the safe and efficient operation of electrified railway systems. However, detecting CSCs in images presents significant challenges due to the scarcity of labeled data, the presence of complex and diverse backgrounds, and the difficulties associated with multi-scale variations. To tackle these issues, this paper introduces a novel detection framework designed explicitly for CSCs. First, a Siamese-based self-supervised learning framework is designed as a pre-training strategy to reduce the reliance on labeled data, effectively leveraging unlabeled images and significantly lowering annotation costs. This pre-training approach enables the model to focus on identifying and extracting relevant features from prior knowledge, honing its ability to discern key patterns and structures within the data. Second, the Vision Attention-based Intrascale Feature Interaction (Vision-AIFI) and Relation Vision Module (RVM) are proposed to enhance the model, which can strengthen its ability to extract multi-scale features and effectively address challenges posed by complex backgrounds and scale variations. Third, a Dempster–Shafer (DS) evidence theory-based detection head is inserted to improve classification confidence and localization precision, ensuring accurate detection results in complex inspection scenarios. Finally, a UAV-based dataset for CSCs is constructed and validation experiments are performed. To evaluate the model, we used several standard COCO metrics, including mAP (77.84), APs (67.84), APm (70.31), and APl (90.04). In addition, the framework is further evaluated for Domain Generalization, which can demonstrate its strong adaptability and high detection accuracy for real-world CSC detection tasks.
Liang et al. (Wed,) studied this question.