Superpixel-Guided Multi-Type Rail Segmentation via Contextual Information Aggregation

Vision-based anomaly inspection plays a crucial role in the efficient maintenance of millions of kilometers of railway, with rail segmentation, a key step in such anomaly detection for providing localization prior. However multi-type rails, those involved in crossings and connections, have highly variable patterns, greatly restricting the performance of standard (straight) rail segmentation methods. Semantic segmentation helps to deal with complex railway scenes and variable patterns, however the noise sensitivity, intra-class differences, and inter-class similarities still challenge the segmentation. Superpixel segmentation can aggregate local similar pixels with precise boundaries, which can offer a weak prior for semantic segmentation for boundary information modeling, intra-class aggregation, and inter-class differentiation, however how to integrate superpixel-level guidance to advance rail segmentation is still challenging. This paper proposes a two-stage transformer-Convolutional Neural Network (CNN)-based segmentation framework. The first stage, Attention-Based Superpixel Segmentation Sub-Network via Boundary Calibration (BCASN), generates railway superpixels by the learning of intra-superpixel consistency and boundary calibration to effectively fit rail boundaries and guide the second-stage rail segmentation. The second stage, Superpixel-Guided Multi-Type Rail Segmentation Sub-Network via Contextual Information Aggregation (CIASSN), captures railway semantics via global and cross-scale context construction, aggregates rail features via directional guidance and structured prior, and makes comprehensive segmentation decisions at superpixel and pixel scales with the learning of superpixel-level context and classification. The experiments demonstrate that the proposed solution achieves 98.71% overall accuracy, 98.44% mIoU, and 87.33% boundary recall in multi-type rail segmentation, significantly extends applicable scenarios, and outperforms all related state-of-the-art methods in rail and road segmentation.