Fully Convolutional Networks for Surface Defect Inspection in Industrial Environment
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In contrast to FCN+CNN, CA-CNN, CNN and other patch-based models, our model does not require finer ground truth.
“In contrast to the FCN+CNN [58], CA-CNN [13], CNN [14] and other patch-based models, our model does not require finer ground truth.”
From A Compact Convolutional Neural Network for Surface Defect Inspection (2020) · cites this paper as [58]
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- Authors
- Yu, Z., Wu, X., & Gu, X.
- Affiliations
- Harbin Institute of Technology (CN)
- Corresponding author
- Wu, Xiaojun
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2017
- Pages
- 417-426
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2762457108
- Cited by
- 77
- References
- 17
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 4 · 2023: 10 · 2022: 9 · 2021: 16 · 2020: 14 · 2019: 9 · 2018: 4
- FWCI
- 10.84
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- Lecture notes in computer science (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- surface defect detection, industrial inspection, fully convolutional network, two-stage detection, lightweight segmentation, patch-based training, limited training data
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