Automatic Rail Component Detection Based on AttnConv-net

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

3 Scopus Citations
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Original languageEnglish
Pages (from-to)2379-2388
Journal / PublicationIEEE Sensors Journal
Issue number3
Online published3 Dec 2021
Publication statusPublished - 1 Feb 2022


The automatic detection of major rail components using railway images is beneficial to ensure the rail transport safety. In this paper, we propose an attention-powered deep convolutional network (AttnConv-net) to detect multiple rail components including the rail, clips, and bolts. The proposed method consists of a deep convolutional neural network (DCNN) as the backbone, cascading attention blocks (CAB), and two feed forward networks (FFN). Two types of positional embedding are applied to enrich information in latent features extracted from the backbone. Based on processed latent features, the CAB aims to learn the local context of rail components including their categories and component boundaries. Final predictions of categories and bounding boxes are generated via two feed forward networks implemented in parallel. To enhance the detection of small components, various data augmentation methods are employed in training process. The effectiveness of the proposed AttnConv-net is validated with one real dataset and another synthesized dataset. Compared with classic convolutional neural network based methods, our proposed method simplifies the detection pipeline by eliminating the need of prior- and post-processing, which offers a new speed-quality solution to enable faster and more accurate image-based rail component detections.

Research Area(s)

  • Attention mechanism, data augmentation, deep learning, Fasteners, Feature extraction, rail component detection, Rail transportation, Rails, railway inspection, Sensors, Task analysis, Training

Citation Format(s)

Automatic Rail Component Detection Based on AttnConv-net. / Wang, Tiange; Zhang, Zijun; Yang, Fangfang et al.
In: IEEE Sensors Journal, Vol. 22, No. 3, 01.02.2022, p. 2379-2388.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review