RTLSeg: A novel multi-component inspection network for railway track line based on instance segmentation

Abstract

The condition monitoring of railway track line is one of the fundamental tasks to ensure the safety of the railway transportation system. Railway track line is mainly made up of tracks, fasteners, bolts, backing plates, and so on. Given the requirements for rapid and accurate inspection, an innovative and intelligent method for multi-component identification and common defect detection of railway track line is investigated based on instance segmentation. More specifically, a railway track line image (RTL-I) dataset is constructed and annotated manually in this paper. After that, based on the work of YOLACT and YOLACT++, combined with prior knowledge, a railway track line image segmentation model (RTLSeg for short) is proposed. Firstly, taking the characteristics of the objects in the RTL-I dataset, preset anchors are redesigned and a feature enhanced module is introduced in the prediction head to improve the detection and segmentation accuracy of the model. Secondly, to strengthen the internal information propagation within the model, PaFPN (path aggregation feature pyramid network) is applied instead of FPN in RTLSeg. Thirdly, with the help of CoordConv, Coord-Protonet is presented to add position awareness explicitly to the model for more robust and higher quality prototype masks. Finally, to further improve the model performance, the attention mechanism is explored and a novel spatial attention-guided bounding box branch is employed in the enhanced prediction head. Both quantitative and qualitative experimental results show that the proposed method is feasible in detecting and segmenting multi-component and common defects of railway track line, and outperforms the compared baseline models. In particular, RTLSeg is able to achieve 91.35 bbox mAP and 91.60 mask mAP with the customized dataset. Meanwhile, the average inference speed reaches 13.07 fps. The average detection accuracy and recall are 100% and 99.83%, respectively. Furthermore, the effectiveness of each optimized part of the proposed RTLSeg model is demonstrated by additional ablation study.