A real-time defective pixel detection system for LCDs using deep learning based object detectors

ÇELİK A., KÜÇÜKMANİSA A., Sumer A., Celebi A., URHAN O.

JOURNAL OF INTELLIGENT MANUFACTURING, vol.33, no.4, pp.985-994, 2022 (SCI-Expanded, Scopus) identifier identifier

  • Publication Type: Article / Article
  • Volume: 33 Issue: 4
  • Publication Date: 2022
  • Doi Number: 10.1007/s10845-020-01704-9
  • Journal Name: JOURNAL OF INTELLIGENT MANUFACTURING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Compendex, Computer & Applied Sciences, INSPEC
  • Page Numbers: pp.985-994
  • Keywords: Pixel defect, Defect detection, LCD, Deep learning, RECOGNITION
  • Kocaeli University Affiliated: Yes

Abstract

The presence of pixel defects on the screens of LCD-based products (TV, tablet, phone, etc.) is unacceptable given the consumer expectations. Therefore, these defects should be detected before the product reaches the user during the production stage. Visual inspections are mostly performed by human operators in the production. These inspections are error prone and not efficient in terms of consumed time. For this reason, computer visionbased approaches are started to find applications in this kind of problems. This paper presents an image acquisition system and a detailed analysis of deep learningbased object detectors for LCD pixel defect detection problem. Experimental results show that the proposed methods can be a powerful alternative to operator control by providing more efficient use of time, human, financial resources and betterquality standards in TV production industry.