Multispectral visual detection method for conveyor belt longitudinal tear

Chengcheng Hou, Tiezhu Qiao, Haitao Zhang, Yusong Pang, Xiaoyan Xiong

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)
2 Downloads (Pure)

Abstract

As an important part of modern coal mine production, conveyor belts are widely used in the coal collection and transportation. In order to ensure the safe operation of the coal mine conveyor belt and solve the drawbacks of the existing conveyor belt longitudinal tear detection technology, a multispectral visual detection method for conveyor belt longitudinal tear is proposed in this paper. The experimental results show that the multispectral visual detection method not only can identify the conveyor belt longitudinal tear, but also accurately classifies and identify other states of the conveyor belt. The accuracy of multispectral visual detection method is over 90.06%, and the precision of longitudinal tearing recognition is over 92.04%. The proposed method is verified to meet the requirements of reliability and real-time in the industrial field.

Original languageEnglish
Pages (from-to)246-257
JournalMeasurement: Journal of the International Measurement Confederation
Volume143
DOIs
Publication statusPublished - 2019

Keywords

  • Conveyor belt
  • Image fusion
  • Longitudinal tear
  • Multispectral visual detection

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