基于卷积神经网络和投票机制的轨道板裂缝检测

doi:10.3969/j.issn.0255-8297.2021.04.010

Abstract

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.

Key words: crack detection, convolutional neural network (CNN), voting mechanism, training tactic, deep learning

CLC Number:

TN911.73

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.

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

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