RFS-Net: Railway Track Fastener Segmentation Network With Shape Guidance
Shixiang Su, Сонглин Ду, Xuan Wei, Xiaobo Lu
Abstract
The fastener is one of the main components of a rail track system. In recent years, deep learning methods such as image segmentation have greatly boosted the fastener state detection process. However, there is still a need to improve the segmentation accuracy and speed, especially for the fasteners in complex environments. To handle this problem, a fast and accurate fastener semantic segmentation network named RFS-Net is proposed based on shape guidance, which can offer a better speed/accuracy trade-off performance via a very shallow architecture. Specifically, in the encoder, a two-stream structure (i.e., regular stream and shape stream) that processes the fastener and shape image in parallel is introduced. The shape image is created based on the geometric structure of the fastener, and it is served as input to the shape stream to guide the segmentation of the fastener. The decoder integrates deep features from the two-stream encoder and then recovers the shape information by the shape attention blocks with skipping connections. We provide two versions of RFS-Net: RFS-Net_S (1.0M, 1014FPS) and RFS-Net_L (12.01M, 453FPS) on the NVIDIA RTX 3060. Experimental results demonstrate the effectiveness of our method by achieving a promising trade-off between accuracy and inference speed. In particular, our method is faster and more accurate on a challenging dataset, from fast modes: 1014 FPS for RFS-Net_S versus 724 FPS for Segmenter, to high-quality segmentation: better performance than STDC with nearly one percent (92.36% versus 91.48% Mean IoU score).