A multi-scale adaptive framework for high-precision rail track damage detection via StarNet and bidirectional feature pyramid network.

Railway tracks are prone to damage after long-term exposure to harsh environments, posing risks to transportation safety. Traditional manual inspection is inefficient and error-prone, while existing deep learning-based detection models, such as YOLO series, often struggle with small targets, complex backgrounds, and multi-scale damage. To address these challenges, this study proposes an improved track damage detection framework, termed SNBF-YOLO, which integrates the Star Net and BiFPN modules. Star Net adaptively enlarges the receptive field to enhance feature representation, while BiFPN optimizes bidirectional multi-scale feature fusion. Experimental results on a real-world dataset demonstrate that SNBF-YOLO improves precision, recall, and mAP by 19.4, 13.2, and 14.3%, respectively, compared to the baseline YOLOv10n model. The enhanced model achieves higher robustness and computational efficiency, enabling accurate real-time detection of track defects, including fine cracks and missing fasteners. Despite these advances, the study is limited by the scale and diversity of the dataset, which may affect generalization in more diverse conditions (e.g., rain, snow, or severe corrosion). Future work will focus on enlarging the dataset and exploring lightweight deployment strategies to further improve real-world adaptability.