The DOI matches its Crossref record; 33 of the 34 references it cites were found in a registry or an index.
Automated defect inspection of LED chip using deep convolutional neural network
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“However, the above deep learning-based industrial defect detection methods do not specifically optimize the model's ability to extract and fuse features, which is crucial for defect detection.”
From A deep learning model for steel surface defect detection (2024) · cites this paper as [18]
Citations
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Cited by
Sorted by citations
Segmentation-based deep-learning approach for surface-defect detection
Tabernik, D., Šela, S., Skvarč, J., & Skočaj, D. (2019) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-019-01476-x · Cited by 996
“The proposed LEDNet showed a significant improvement in the defect-detection rate compared to traditional methods.”
InfluentialDeep Metallic Surface Defect Detection: The New Benchmark and Detection Network
Lv, X., Duan, F., Jiang, J.-J., Fu, X., & Gan, L. (2020) · Sensors · doi:10.3390/s20061562 · Cited by 643
“Lin et al. [33] proposed a LEDNet to exploit image-annotation and large batch sizes.”
BackgroundAutomatic Metallic Surface Defect Detection and Recognition with Convolutional Neural Networks
Tao, X., Zhang, D., Ma, W., Liu, X., & Xu, D. (2018) · Applied Sciences · doi:10.3390/app8091575 · Cited by 471
“[34] built a convolutional neural network (CNN) for light emitting diode (LED) chip defect inspection.”
BackgroundVisual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY
Czimmermann, T., Ciuti, G., Milazzo, M., Chiurazzi, M., Roccella, S., Oddo, C. M., & Dario, P. (2020) · Sensors · doi:10.3390/s20051459 · Cited by 441
“Lin, Hui et al. [214] developed a CNN called LEDNet to detect and classify defect on LED chips, where they achieved relatively low inaccuracy of 5.05%.”
BackgroundMethodInfluentialMixed supervision for surface-defect detection: From weakly to fully supervised learning
Božič, J., Tabernik, D., & Skočaj, D. (2021) · Computers in Industry · doi:10.1016/j.compind.2021.103459 · Cited by 377
“Fully supervised defect detection Several related works explored the use of deep-learning for industrial anomaly detection and categorisation [24, 18, 13, 42, 35, 27, 17, 38, 14], including the early work of Masci et al.”
Influential
Metadata
- Authors
- Lin, H., Li, B., Wang, X., Shu, Y., & Niu, S.
- Affiliations
- State Key Laboratory of Digital Manufacturing Equipment and Technology (CN)
- Huazhong University of Science and Technology (CN)
- Corresponding author
- Li, Bin
- Venue
- Journal of Intelligent Manufacturing
- Published
- 2018-03-29
- Volume
- 30
- Issue
- 6
- Pages
- 2525-2534
- ISSN
- 0956-5515 (ISSN-L)
- 1572-8145
- OpenAlex
- W2794550100
- Semantic Scholar
- 358b9707c2d45c06e23a264d21390c2ed5a1d7ee
- Cited by
- 241
- References
- 34
- Citations by year
- 2026: 13 · 2025: 25 · 2024: 34 · 2023: 36 · 2022: 36 · 2021: 40 · 2020: 40 · 2019: 11 · 2018: 6
- FWCI
- 19.03
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Intelligent Manufacturing (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- The 8th batch of 3551 Optics Valley Talent Schema in East Lake Hi-tech Zone- LED chip automatic detection sorting key technology and sets of equipment development and industrialization
- 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
- defect localization, class activation mapping, scratch marks, automated visual inspection
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