SFAB-Net: semantic segmentation network for railway track surface defects based on spatial fusion and adaptive bottleneck feature enhancement

Under the long-term action of train loads and complex environmental conditions, the surfaces of railway tracks are prone to defects such as cracks, spalling, and pitting, which seriously threaten the safety of railway operations. Semantic segmentation can achieve pixel-level positioning and morphological characterization of defects. However, existing methods still struggle to model strongly directional structures and multi-scale defects while maintaining a balance between accuracy and efficiency in rail-surface inspection. To address the above issues, this paper proposes a lightweight semantic segmentation network for railway track surface defects (SFAB-Net) based on spatial fusion and adaptive bottleneck feature enhancement. This network effectively characterizes the features of slender cracks along the rail direction using the direction-sensitive Spatial-Fusion module and combines them with the simplified spatial pyramid pooling module to achieve multi-scale context aggregation. In the decoding stage, an adaptive feature reconstruction mechanism and spatial-channel joint attention are introduced to enhance multi-scale feature fusion and suppress background interference. Experimental results on the NEU-DET dataset and a self-built rail surface image dataset show that SFAB-Net outperforms several representative methods in segmentation accuracy and robustness, and has strong potential for engineering applications.

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