Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network

Junwen Chen, Zhigang Liu, Hongrui Wang, Alfredo Nunez, Zhiwei Han

Research output: Contribution to journalArticleScientificpeer-review

148 Citations (Scopus)
465 Downloads (Pure)

Abstract

The excitation and vibration triggered by the long-term operation of railway vehicles inevitably result in defective states of catenary support devices. With the massive construction of high-speed electrified railways, automatic defect detection of diverse and plentiful fasteners on the catenary support device is of great significance for operation safety and cost reduction. Nowadays, the catenary support devices are periodically captured by the cameras mounted on the inspection vehicles during the night, but the inspection still mostly relies on human visual interpretation. To reduce the human involvement, this paper proposes a novel vision-based method that applies the deep convolutional neural networks (DCNNs) in the defect detection of the fasteners. Our system cascades three DCNN-based detection stages in a coarse-to-fine manner, including two detectors to sequentially localize the cantilever joints and their fasteners and a classifier to diagnose the fasteners' defects. Extensive experiments and comparisons of the defect detection of catenary support devices along the Wuhan-Guangzhou high-speed railway line indicate that the system can achieve a high detection rate with good adaptation and robustness in complex environments.

Original languageEnglish
Pages (from-to)257-269
Number of pages13
JournalIEEE Transactions on Instrumentation and Measurement
Volume67
Issue number2
DOIs
Publication statusPublished - 2018

Keywords

  • Automatic defect detection
  • Cameras
  • catenary support device
  • Computer architecture
  • deep convolutional neural network (DCNN)
  • Detectors
  • fastener
  • Fasteners
  • Feature extraction
  • high-speed railway.
  • Rail transportation

Fingerprint Dive into the research topics of 'Automatic Defect Detection of Fasteners on the Catenary Support Device Using Deep Convolutional Neural Network'. Together they form a unique fingerprint.

Cite this