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

Aslı Çelik, Ayhan Küçükmanisa (), Aydın Sümer, Aysun Taşyapı Çelebi and Oğuzhan Urhan
Additional contact information
Aslı Çelik: Kocaeli University
Ayhan Küçükmanisa: Kocaeli University
Aydın Sümer: Kocaeli University
Aysun Taşyapı Çelebi: Kocaeli University
Oğuzhan Urhan: Kocaeli University

Journal of Intelligent Manufacturing, 2022, vol. 33, issue 4, No 6, 985-994

Abstract: 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.

Keywords: Pixel defect; Defect detection; LCD; Deep learning (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s10845-020-01704-9 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:joinma:v:33:y:2022:i:4:d:10.1007_s10845-020-01704-9

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/10845

DOI: 10.1007/s10845-020-01704-9

Access Statistics for this article

Journal of Intelligent Manufacturing is currently edited by Andrew Kusiak

More articles in Journal of Intelligent Manufacturing from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().