A sparse representation based fast detection method for surface defect detection of bottle caps

Wenju Zhou, Minrui Fei, Huiyu Zhou, Kang Li

Research output: Contribution to journalArticlepeer-review

42 Citations (Scopus)

Abstract

A practical machine-vision-based system is developed for fast detection of defects occurring on the surface of bottle caps. This system can be used to extract the circular region as the region of interests (ROI) from the surface of a bottle cap, and then use the circular region projection histogram (CRPH) as the matching features. We establish two dictionaries for the template and possible defect, respectively. Due to the requirements of high-speed production as well as detecting quality, a fast algorithm based on a sparse representation is proposed to speed up the searching. In the sparse representation, non-zero elements in the sparse factors indicate the defect's size and position. Experimental results in industrial trials show that the proposed method outperforms the orientation code method (OCM) and is able to produce promising results for detecting defects on the surface of bottle caps.
Original languageEnglish
Pages (from-to)406-414
Number of pages9
JournalNeurocomputing
Volume123
DOIs
Publication statusPublished - 10 Jan 2014

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Cognitive Neuroscience

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