Deep Active Learning for Surface Defect Detection
Open accessAbstract
Most of the current object detection approaches deliver competitive results with an assumption that a large number of labeled data are generally available and can be fed into a deep network at once. However, due to expensive labeling efforts, it is difficult to deploy the object detection systems into more complex and challenging real-world environments, especially for defect detection in real industries. In order to reduce the labeling efforts, this study proposes an active learning framework for defect detection. First, an Uncertainty Sampling is proposed to produce the candidate list for annotation. Uncertain images can provide more informative knowledge for the learning process. Then, an Average Margin method is designed to set the sampling scale for each defect category. In addition, an iterative pattern of training and selection is adopted to train an effective detection model. Extensive experiments demonstrate that the proposed method can render the required performance with fewer labeled data.
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Asia (China), North America (United States of America), and Europe (Germany) are producing the main advances in active learning for object detection research.
“An analysis of the research groups or technological companies working in AL for OD showed that Asia (China), North America (United States of America), and Europe (Germany) are producing the main advances in this research field (e.g., [5,36,55]), as can be seen in Figure 5.”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [55]
The Abstract and §3.1 describe this paper’s active-learning method for surface defect detection, so it is a relevant example of an advance in the field. Our copy does not establish the authors’ country or compare advances by region. The statement about which regions produce the main advances is the citing authors’ analysis, not a finding reported by this source. (LLM, conf=0.78)
Experimental — accuracy not yet measuredOther applications such as document layout detection, pose estimation, and surface defect detection remain under-explored.
“Other applications such as document layout detection [14,17], pose estimation [7], and surface defect detection [55] remain under-explored, as well.”
From Ten Years of Active Learning Techniques and Object Detection: A Systematic Review (2023) · cites this paper as [55]
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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 56,035 · see citing works
You Only Look Once: Unified, Real-Time Object Detection
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803 · see citing works
(2016) · Cited by 31,590 · see citing works
SSD: Single Shot MultiBox Detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., & Berg, A. C. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46448-0_2 · Cited by 22,128 · see citing works
YOLO9000: Better, Faster, Stronger
Redmon, J., & Farhadi, A. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.690 · Cited by 19,892 · see citing works
YOLOv3: An Incremental Improvement
Redmon, J., & Farhadi, A. (2018) · arXiv · doi:10.48550/arxiv.1804.02767 · Cited by 17,141 · see citing works
OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks
Sermanet, P., Eigen, D., Zhang, X., Mathieu, M., Fergus, R., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6229 · Cited by 2,680 · see citing works
Song, K., & Yan, Y. (2013) · Applied Surface Science · doi:10.1016/j.apsusc.2013.09.002 · Cited by 1,026 · see citing works
Manufacturing of Lightweight Components by Metal Forming
Kleiner, M., Geiger, M., & Klaus, A. A. (2003) · CIRP Annals · doi:10.1016/s0007-8506(07)60202-9 · Cited by 598 · see citing works
A Survey of Active Learning Algorithms for Supervised Remote Sensing Image Classification
Tuia, D., Volpi, M., Copa, L., Kanevski, M., & Muñoz-Marı́, J. (2011) · IEEE Journal of Selected Topics in Signal Processing · doi:10.1109/jstsp.2011.2139193 · Cited by 557 · see citing works
Advanced Steel Microstructural Classification by Deep Learning Methods
Azimi, S. M., Britz, D., Engstler, M., Fritz, M., & Mücklich, F. (2018) · Scientific Reports · doi:10.1038/s41598-018-20037-5 · Cited by 507 · see citing works
A Review of Recent Advances in Surface Defect Detection using Texture analysis Techniques
Xie, X. (2008) · ELCVIA Electronic Letters on Computer Vision and Image Analysis · doi:10.5565/rev/elcvia.268 · Cited by 500 · see citing works
A Generic Deep-Learning-Based Approach for Automated Surface Inspection
Ren, R., Hung, T., & Tan, K. C. (2018) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2017.2668395 · Cited by 485 · see citing works
Multi-Class Active Learning by Uncertainty Sampling with Diversity Maximization
Yang, Y., Ma, Z., Nie, F., Chang, X., & Hauptmann, A. G. (2014) · International Journal of Computer Vision · doi:10.1007/s11263-014-0781-x · Cited by 448 · see citing works
Steel defect classification with Max-Pooling Convolutional Neural Networks
Masci, J., Meier, U., Ciresan, D., Schmidhuber, J., & Fricout, G. (2012) · The 2012 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2012.6252468 · Cited by 345 · see citing works
Incorporating Diversity and Density in Active Learning for Relevance Feedback
Xu, Z., Akella, R., & Zhang, Y. (2007) · Springer eBooks · doi:10.1007/978-3-540-71496-5_24 · Cited by 194 · see citing works
Zhu, J., Wang, H., Yao, T., & Tsou, B. K. (2008) · International Conference on Computational Linguistics · doi:10.3115/1599081.1599224 · Cited by 180 · see citing works
Deep Active Learning for Civil Infrastructure Defect Detection and Classification
Feng, C., Liu, M.-Y., Kao, C.-C., & Lee, T. (2017) · ASCE International Conference on Computing in Civil Engineering (i3CE) · doi:10.1061/9780784480823.036 · Cited by 164 · see citing works
A High-Precision Loose Strands Diagnosis Approach for Isoelectric Line in High-Speed Railway
Liu, Z., Wang, L., Li, C., & Han, Z. (2017) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2017.2774242 · Cited by 84 · see citing works
Pattern Deep Region Learning for Crack Detection in Thermography Diagnosis System
Hu, J., Xu, W., Gao, B., Tian, G. Y., Wang, Y., 呉, 勇., Yin, Y., & Chen, J. (2018) · Metals · doi:10.3390/met8080612 · Cited by 43 · see citing works
Handwritten English Word Recognition Based on Convolutional Neural Networks
Yuan, A., Bai, G., Yang, P., Guo, Y., & Zhao, X. (2012) · International Conference on Frontiers in Handwriting Recognition · doi:10.1109/icfhr.2012.210 · Cited by 39 · see citing works
A Surface Defect Detection Based on Convolutional Neural Network
Wu, X., Cao, K., & Gu, X. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-68345-4_17 · Cited by 24 · see citing works
Dynamic batch mode active learning
Chakraborty, S., Balasubramanian, V. N., & Panchanathan, S. (2011) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2011.5995715 · Cited by 20 · see citing works
Uncertainty metric in model-based eddy current inversion using the adaptive Monte Carlo method
Fan, M., Wu, G., Cao, B., Sarkodie-Gyan, T., Li, Z., & Tian, G. Y. (2019) · Measurement · doi:10.1016/j.measurement.2019.01.004 · Cited by 16 · see citing works
Flagged first, then by citations
2 of the 26 references couldn’t be matched to a known paper.
Research works printed but not linked: 1
- [3]Liu R.-X. Yao M.-H. Wang X.-B. Defects Detection Based on Deep Learning and Transfer Learning Metal. Min. Ind. 2015 Available online: https://www.researchgate.net/publication/285367015_Defects_detection_based_on_deep_learning_and_transfer_learning (accessed on 12 March 2020)
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State of the Art in Defect Detection Based on Machine Vision
Ren, Z., Fang, F., Yan, N., & Wu, Y. (2021) · International Journal of Precision Engineering and Manufacturing-Green Technology · doi:10.1007/s40684-021-00343-6 · Cited by 851 · see citing works
A Survey on Active Deep Learning: From Model Driven to Data Driven
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A steel surface defect inspection approach towards smart industrial monitoring
Hao, R., Lu, B., Cheng, Y., Li, X., & Huang, B. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01670-2 · Cited by 182 · see citing works
Research Progress of Automated Visual Surface Defect Detection for Industrial Metal Planar Materials
Fang, X., Luo, Q., Zhou, B., Li, C., & Tian, L. (2020) · Sensors · doi:10.3390/s20185136 · Cited by 166 · see citing works
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Prunella, M., Scardigno, R. M., Buongiorno, D., Brunetti, A., Longo, N., Carli, R., Dotoli, M., & Bevilacqua, V. (2023) · IEEE Access · doi:10.1109/access.2023.3271748 · Cited by 162 · see citing works
Surface defect detection of civil structures using images: Review from data perspective
Guo, J., Liu, P., Xiao, B., Deng, L., & Wang, Q. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.105186 · Cited by 121 · see citing works
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Development of Deep Learning Model for the Recognition of Cracks on Concrete Surfaces
Le, T.-T., Nguyen, V.-H., & Le, M. V. (2021) · Applied Computational Intelligence and Soft Computing · doi:10.1155/2021/8858545 · Cited by 52 · see citing works
Computer vision defect detection on unseen backgrounds for manufacturing inspection
Mezher, A. M., & Marble, A. E. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122749 · Cited by 47 · see citing works
Data-driven approach for AI-based crack detection: techniques, challenges, and future scope
Chakurkar, P. S., Vora, D. R., Patil, S., Mishra, S., & Kotecha, K. V. (2023) · Frontiers in Sustainable Cities · doi:10.3389/frsc.2023.1253627 · Cited by 38 · see citing works
Wang, J., & Lee, S. (2021) · Applied Sciences · doi:10.3390/app11156721 · Cited by 36 · see citing works
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Ni, Y.-S., Chen, W., Liu, Y., Wu, M.-H., & Guo, J.-I. (2024) · Sensors · doi:10.3390/s24175737 · Cited by 8 · see citing works
Aşar, E., & Özgür, A. (2024) · Engineering materials · doi:10.1007/978-3-031-57468-9_3 · Cited by 8 · see citing works
Balcıoğlu, Y. S., Sezen, B., Çerasi, C. Ç., & Huang, S. H. (2023) · Electronics · doi:10.3390/electronics12040825 · Cited by 7 · see citing works
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Torres-Torres, M., Soto-Díaz, R., Gutiérrez, J. E., & Valls, A. (2026) · MRS Advances · doi:10.1557/s43580-026-01566-y · Cited by 3 · see citing works
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Akın, E., AYDIN, A., & Aydın, İ. (2023) · Research Square · doi:10.21203/rs.3.rs-3231635/v1
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- Authors
- Lv, X., Duan, F., Jiang, J.-J., Fu, X., & Gan, L.
- Affiliations
- Tianjin University (CN)
- The State Key Lab of Precision Measuring Technology and Instruments, Tianjin University, Tianjin 300072, China
- Corresponding author
- Duan, Fajie
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2020-03-16
- Volume
- 20
- Issue
- 6
- Pages
- 1650
- ISSN
- 1424-8220 (ISSN-L)
- PMC
- PMC7146140
- OpenAlex
- W3012089510
- Cited by
- 58
- References
- 26
- Citations by year
- 2026: 10 · 2025: 9 · 2024: 11 · 2023: 15 · 2022: 2 · 2021: 8 · 2020: 3
- FWCI
- 4.46
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Sensors (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Natural Science Foundations of China (No.61905175, 51775377, 61971307)
- National key research and development plan (2017YFF0204800)
- the Tianjin Natural Science Foundations of China (No.17JCQNJC01100)
- Equipment pre-research field fund (61405180505, 61400040303)
- Science and Technology on Underwater Information and Control Laboratory (6142218081811)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- active learning (machine learning), surface defect detection, uncertainty sampling, annotation cost reduction, industrial inspection, object detection, semi-supervised learning
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