Abstract
Rail fastener status recognition and detection are key steps in the inspection of the rail area status and function of real engineering projects. With the development of and widespread interest in image processing techniques and deep learning theory, detection methods that combine the two have yielded promising results in practical detection applications. In this paper, a semantic-segmentation-based algorithm for the state recognition of rail fasteners is proposed. On the one hand, we propose a functional area location and annotation method based on a salient detection model and construct a novel slab-fastclip-type rail fastener dataset. On the other hand, we propose a semantic-segmentation-framework-based model for rail fastener detection, where we detect and classify rail fastener states by combining the pyramid scene analysis network (PSPNet) and vector geometry measurements. Experimental results prove the validity and superiority of the proposed method, which can be introduced into practical engineering projects.
Highlights
A rail fastener is a fixed coupling part that prevents horizontal and vertical offsets in rails. us, rail fastener detection can be used for maintaining the stability of railway systems and ensuring the safety of trains
To solve the problems of existing approaches, we present a novel semantic-segmentation-based rail fastener state recognition algorithm. e contributions of our work are as follows: (1) To reduce the reliance of traditional deep learning methods on manual annotation, we provide a semiautomatic method for locating and annotating rail fasteners based on saliency detection. e experimental results show that the method can accurately locate and segment fastener pop-up regions and generate accurate pixel-level annotations of the rail fastener image, reducing the cost of the manual annotation of functional regions and improving the efficiency by 25 times that of the traditional manual annotation process
We aim to address the limitations of traditional rail fastener detection methods and deep learning theory in engineering applications
Summary
A rail fastener is a fixed coupling part that prevents horizontal and vertical offsets in rails. Erefore, automatic rail fastener detection has attracted increasing attention from researchers. Deep learning theory has received increasing attention in target detection and image segmentation works and has been successfully applied to rail fastener detection [12,13,14]. These efforts are often heavily dependent on timeconsuming and expensive manual annotations
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