Special Issue on CCF NCCA 2020

Crack Detection of Track Slab Based on Convolutional Neural Network and Voting Mechanism

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  • 1. School of Computer Science and Information Engineering, Shanghai Institute of Technology, Shanghai 201418, China;
    2. School of Railway Transportation, Shanghai Institute of Technology, Shanghai 201418, China

Received date: 2020-09-03

  Online published: 2021-08-04

Abstract

There are misdetections and missed detections in the crack detection of track slab or in crack pictures taken at night as using existing detection methods. For the problem, an improved method based on convolutional neural network (CNN) is proposed. In this way, high-level feature maps are divided into different groups of vectors whose feature expression would be emphasized by attention mechanism subsequently. Final confidence is accounted by aggregating the predict result of weak classifiers dynamically. With the favor of voting mechanism, predict error is reduced and robustness of model is improved effectively. Experiment results show that the improved method gains a prediction improvement of 1.6% in crack dataset and an improvement of 2.8% in CTFAR-10 dataset, in spite of the reduction of model parameters.

Cite this article

LI Wenju, HE Maoxian, ZHANG Yaoxing, CHEN Huiling, LI Peigang . Crack Detection of Track Slab Based on Convolutional Neural Network and Voting Mechanism[J]. Journal of Applied Sciences, 2021 , 39(4) : 627 -640 . DOI: 10.3969/j.issn.0255-8297.2021.04.010

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