Hot-Rolled, Heavy-Rail Image Recognition Based on Deep-Learning Network

Authors

  • Xie Changgui Chongqing Vocational Institute of Engineering, Chongqing, 402260, China https://orcid.org/0000-0003-4133-3015
  • Xu Hao Chongqing Vocational Institute of Engineering, Chongqing, 402260, China
  • Liu Yuxi Chongqing Vocational Institute of Engineering, Chongqing, 402260, China
  • Chen Ping Chongqing University, Chongqing, 400044, China

DOI:

https://doi.org/10.13052/jwe1540-9589.20513

Keywords:

heavy rail; deep learning; defect recognition; error recognition rate; network

Abstract

A new method for image-defect recognition is proposed that is based on a convolution network with repeated stacking of small convolution kernels and a maximum pooling layer. By improving the speed and accuracy of image-defect recognition, this new method can be applied to image recognition such as heavy-rail images with high noise and many types of defects. The experimental results showed that the new algorithm effectively improved the accuracy of heavy-rail image-defect recognition. As evidenced by the simulation study, the proposed method has a lower error rate in heavy-rail image recognition than traditional algorithms, and the method may also be applied to defect recognition of nonlinear images under strong noise conditions. Its robustness and nonlinear processing ability are impressive, and the method is featured with high theoretical depth and important application value.

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Author Biography

Xie Changgui, Chongqing Vocational Institute of Engineering, Chongqing, 402260, China

Xie Changgui was born in 1984 and obtained Ph.D. in school of mechanical engineering at Chongqing University in December 2012. He is now working as an associate professor in a famous Vocational College in Chongqing. His research area is about equipment diagnosis and signal processing, artificial intelligence etc. He has presided over many national and provincial projects.

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Published

2021-08-26

Issue

Section

Advanced Practice in Web Engineering