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Cited by 93Industrial Vision Systems and Defect DetectionAdvanced Neural Network ApplicationsInfrastructure Maintenance and Monitoring

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A YOLO-based Real-time Packaging Defect Detection System

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Vu, Thi-Thu-Huyen, Pham, Dinh-Lam, Chang, Tai-Woo·2023·Procedia Computer Science
DOI 10.1016/j.procs.2022.12.285
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Abstract

Managing the quality of products is one of the primary concerns in manufacturing production to obtain better operational efficiency in factories. In recent years, there have been numerous different approaches for improving product quality management in manufacturing. Each method has certain advantages and limitations, and the common goal is to bring the best efficiency in managing product quality before delivering them to consumers. In this paper, we introduce an approach to creating a real-time packaging defect detection system based on deep learning techniques intending to automatically detect defective packaged products in industrial quality control of packages. To be more precise, we present a real-time defect detection system to help classify product quality automatically based on the YOLO (You only look once) algorithm. The system can be integrated into factories and production lines, helping to optimize efficiency and save operating costs.

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Metadata

Authors
Vu, T.-T.-H., Pham, D.-L., & Chang, T.-W.
Affiliations
Kyonggi University (KR)
Corresponding author
Chang, Tai-Woo
ORCID
Venue
Procedia Computer Science
Publisher
Elsevier BV
Published
2023
Volume
217
Pages
886-894
ISSN
1877-0509 (ISSN-L)
DOI
10.1016/j.procs.2022.12.285
OpenAlex
W4315782998
Cited by
93
References
0
Citations by year
2026: 28 · 2025: 32 · 2024: 26 · 2023: 7
FWCI
40.38
Type
conference paper
Language
en
Open access
Licence
Locations
Procedia Computer Science (publishedVersion, cc-by)
Topics
  • Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Keywords
YOLO, real-time object detection, manufacturing quality control, automated visual inspection

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