Defect detection in printed circuit boards using you-only-look-once convolutional neural networks
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In this study, a deep learning algorithm based on the you-only-look-once (YOLO) approach is proposed for the quality inspection of printed circuit boards (PCBs). The high accuracy and efficiency of deep learning algorithms has resulted in their increased adoption in every field. Similarly, accurate detection of defects in PCBs by using deep learning algorithms, such as convolutional neural networks (CNNs), has garnered considerable attention. In the proposed method, highly skilled quality inspection engineers first use an interface to record and label defective PCBs. The data are then used to train a YOLO/CNN model to detect defects in PCBs. In this study, 11,000 images and a network of 24 convolutional layers and 2 fully connected layers were used. The proposed model achieved a defect detection accuracy of 98.79% in PCBs with a batch size of 32.
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A deep learning model for detecting defects in printed circuit boards got up to 98.79% accuracy by training on a TITAN V GPU.
“In [39] the authors developed a deeplearning model for detecting defects in printed circuit boards(PCBs), that got up to an accuracy of 98.79% by training it on a TITAN V GPU.”
From A review on modern defect detection models using DCNNs – Deep convolutional neural networks (2022) · cites this paper as [39]
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- Authors
- Adibhatla, V. A., Chih, H.-C., Hsu, C.-C., Cheng, J., Abbod, M. F., & Shieh, J.-S.
- Affiliations
- Yuan Ze University (TW)
- Department of Advanced manufacturing system, Boardtek Electronics Corporation, Taoyuan 328454, Taiwan
- Brunel University of London (GB)
- Corresponding author
- Abbod, Maysam F.
- Venue
- Electronics
- Publisher
- MDPI AG
- Published
- 2020-09-22
- Volume
- 9
- Issue
- 9
- Pages
- 1547
- ISSN
- 2079-9292 (ISSN-L)
- OpenAlex
- W3088490277
- Cited by
- 179
- References
- 35
- Citations by year
- 2026: 19 · 2025: 23 · 2024: 36 · 2023: 42 · 2022: 41 · 2021: 17 · 2020: 1
- FWCI
- 16.10
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Electronics (publishedVersion, cc-by)
- Brunel University Research Archive (BURA) (Brunel University London) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- 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
- printed circuit board defect detection, YOLO, object detection, quality inspection, convolutional neural networks, image-based defect classification, automated visual inspection
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