Image data assessment approach for deep learning-based metal surface defect-detection systems
Open accessAbstract
The current trend in automated optical inspection (AOI) systems employs deep learning models to detect defects on a metal surface. The setback of deep learning models is that they are time-consuming because the images obtained after every lighting adjustment must be used to train the deep learning models again and confirm whether the detection results have improved. To save the time spent using datasets to train deep networks, we proposed a comprehensive assessment score that combines defect visibility, visibility distribution, and overexposure based on the operation principles of convolution neural networks. It can be used to assess whether the training image dataset can improve the defect detection rate of the deep learning model such as You Only Look Once (YOLO), Single Shot MultiBox Detector (SSD), and Faster Region-based Convolutional Neural Network (Faster R-CNN) without training defect image datasets. We collected all of the weight combinations with the right prediction results and used linear regression to obtain the optimal weight coefficients. We found that visibility and overexposure had a greater impact on the comprehensive assessment score. We compared the proposed approach with existing image quality assessment methods, including mean absolute error (MAE), peak signal-to-noise ratio (PSNR), universal image quality index (UIQI), natural image quality evaluator (NIQE), perception-based quality evaluator (PIQE), and blind/referenceless image spatial quality evaluator (BRISQUE). The experiment results indicated that our proposed comprehensive assessment score is more correlated to the F2-score of the detection models than the IQA methods by the verification methods of Spearman Rank Correlation Coefficient (SRCC), Pearson Correlation, and Kendall Correlation. Thus, referring to this index during the collection of image data and choosing datasets with the highest score to train the model will produce better detection accuracy.
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The LED light source is the most widely used in the field of steel defect recognition.
“Among them, the LED light source is the most widely used in the field of steel defect recognition [40][41][42].”
From Steel Surface Defect Recognition: A Survey (2022) · cites this paper as [42]
The Lighting Configurations section says the study used two blue LED lights to image defects on metal samples. It does not say LED is the most widely used light source in steel defect recognition; the paper’s experiment concerns anodized aluminum casings, not steel (Experiment section). Page numbers were not available in our copy. (LLM, conf=0.85)
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- HSIEN-I. LIN (Member, IEEE) received the Ph.D. degree in robotics from Purdue Uni- versity, West Lafayette, IN, USA. He is cur- rently a Professor with the Graduate Institute of Automation, National Taipei University of Tech- nology, Taipei, Taiwan, where he researches the human-robot interaction and machine learning. For his research contributions, he was awarded the Young Researcher Award from the National Taipei University of Technology, in 2015, the Young Robotics Researcher Award from the Robotics Society of Taiwan, in 2016, the Young Control Researcher Award from the Chinese Automatic Control Society, in 2017, and the Ta-You Wu Memorial Award from the Ministry of Science and Technology, Taiwan, in 2017.
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- Authors
- Lin, H.-I., & Wibowo, F. S.
- Affiliations
- National Taipei University of Technology (TW)
- Venue
- IEEE Access
- Published
- 2021
- Volume
- 9
- Pages
- 47621-47638
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W3136431771
- Cited by
- 43
- References
- 51
- Citations by year
- 2026: 7 · 2025: 6 · 2024: 12 · 2023: 6 · 2022: 9 · 2021: 3
- FWCI
- 3.87
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- IEEE Access (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Ministry of Science and Technology (MOST 108-2221-E-027-115-MY3)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Video Quality Assessment
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- metal surface defect detection, image quality assessment, automated optical inspection, overexposure, training data selection, image data collection, detection accuracy, YOLO, Faster R-CNN, SSD
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