A Low-Complexity Accurate Ranging Algorithm for a Switch Machine Working Component Based on the Mask RCNN

Abstract

According to the intelligent development needs of railway operation and maintenance, turnout maintenance also needs an efficient and intelligent means of detection. It is the main method used to measure the access depth of static contact manually. In order to change the disadvantages of the low efficiency and strong subjectivity of traditional schemes, a low-complexity accurate ranging algorithm of the Mask RCNN is proposed to measure the on–off working parts. Firstly, the Mask RCNN and an interactive iterative method are used to segment the region of interest accurately twice. Secondly, the graph distortion is corrected according to the vertex mapping principle. Finally, the accurate actual distance is calculated through fitting the linear distance transformation equation. Through the secondary segmentation and correction algorithm, the accurate calculation of a small target is completed. The experimental results show that the algorithm can accurately measure the distance of different working parts; the average processing time is 0.8 s/amplitude and the measurement error is ±1 mm.

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“…Shen et al [ 22 ] proposed an unsound wheat kernel recognition algorithm based on improved Mask R-CNN for the needs of rapid wheat rating. By optimizing the structure of FPN and RPN, and adding attention mechanism, the model can identify unsound wheat kernels faster and more accurately, with an accuracy rate of 86%, a recall rate of 91%, and the inference speed of a single image reaching 7.83 s. Aiming at the problems of low efficiency and low accuracy during the manual maintenance of railway switches, Wei et al [ 23 ] proposed a low-complexity accurate ranging algorithm based on Mask R-CNN. The region of interest is segmented twice through the interactive iterative method, the image distortion is corrected according to the vertex mapping principle, and the accurate actual distance can be calculated by fitting the linear distance transformation equation.…”