Research on conditional characteristics vision real-time detection system for conveyor belt longitudinal tear

Tiezhu Qiao, Xinyu Li, Yusong Pang, Yuxiang Lü, Feng Wang, Baoquan Jin

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
58 Downloads (Pure)

Abstract

Conveyor belt longitudinal tear is one of the most serious problems in coal mining. Existing systems cannot realise lossless and real-time detection for longitudinal tear of conveyor belt. Currently, visual detecting systems are proposed by many researchers and are becoming the future trend. A visual recognition system based on using laser and area light sources is designed in this study, which can recognise and count abrasions, incomplete-tears, and complete-tears. The advantage of the system is to prevent longitudinal tear based on multi-feature information. In the process of detecting conditional characteristics, laser and area light sources are responsible for enhancing contrast between conditional features and conveyor belt surface, meanwhile false corner filtration and single-point feature identification method are designed for improving recognition accuracy of the system. Compared with several current systems, the designed system has a better performance on recognising complex tear characteristics of conveyor belt, thus the problem of starting warning only based on single feature can be effectively avoided.

Original languageEnglish
Pages (from-to)955-960
JournalIET Science, Measurement and Technology
Volume11
Issue number7
DOIs
Publication statusPublished - 2017

Bibliographical note

Accepted Author Manuscript

Keywords

  • belts
  • coal
  • conveyors
  • mining equipment
  • real-time systems

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