A hierarchical learning approach for railway fastener detection using imbalanced samples-Reference-Cited by-同舟云学术

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A hierarchical learning approach for railway fastener detection using imbalanced samples

Published:2021-12 Issue: Volume:186 Page:110240
ISSN:0263-2241
Container-title:Measurement
language:en
Short-container-title:Measurement

Author:

Liu Jianwei,Teng Yun,Shi Bo,Ni Xuefeng,Xiao Weichu,Wang Chao,Liu Hongli

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Applied Mathematics,Electrical and Electronic Engineering,Condensed Matter Physics,Instrumentation

Reference26 articles.

1. Automatic visual detection system of railway surface defects with curvature filter and improved gaussian mixture model;Zhang;IEEE Trans. Instrum. Meas.,2018

2. Recognition and calibration of rail profile under affine-distortion-based point set mapping;Liu;IEEE Trans. Instrum. Meas.,2017

3. A multi-feature hierarchical locating algorithm for hexagon nut of railway fasteners;Peng;IEEE Trans. Instrum. Meas.,2020

4. An efficient direction field-based method for the detection of fasteners on high-speed railways;Yang;Sensors.,2011

5. Automatic fastener classification and defect detection in vision-based railway inspection systems;Feng;IEEE Trans. Instrum. Meas.,2014

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