PGA-Net: Pyramid Feature Fusion and Global Context Attention Network for Automated Surface Defect Detection
Open accessAbstract
Surface defect detection is a critical task in industrial production process. Nowadays, there are lots of detection methods based on computer vision and have been successfully applied in industry, they also achieved good results. However, achieving full automation of surface defect detection remains a challenge, due to the complexity of surface defect, in intraclass. While the defects between interclass contain similar parts, there are large differences in appearance of the defects. To address these issues, this article proposes a pyramid feature fusion and global context attention network for pixel-wise detection of surface defect, called PGA-Net. In the framework, the multiscale features are extracted at first from backbone network. Then the pyramid feature fusion module is used to fuse these features into five resolutions through some efficient dense skip connections. Finally, the global context attention module is applied to the fusion feature maps of adjacent resolution, which allows effective information propagate from low-resolution fusion feature maps to high-resolution fusion ones. In addition, the boundary refinement block is added to the framework to refine the boundary of defect and improve the result of the prediction. The final prediction is the fusion of the five resolutions fusion feature maps. The results of evaluation on four real-world defect datasets demonstrate that the proposed method outperforms the state-of-the-art methods on mean intersection of union and mean pixel accuracy (NEU-Seg: 82.15%, DAGM 2007: 74.78%, MT_defect: 71.31%, Road_defect: 79.54%).
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The steel surface defect detection method based on deep learning is widely used in steel production.
“Therefore, the steel surface defect detection method based on deep learning is widely used in steel production, and more scholars begin to improve and perfect it [13, 14].”
From A New Steel Defect Detection Algorithm Based on Deep Learning (2021) · cites this paper as [13]
Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works
U-Net: Convolutional Networks for Biomedical Image Segmentation
Ronneberger, O., Fischer, P., & Brox, T. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-24574-4_28 · Cited by 99,404 · see citing works
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., & Darrell, T. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298965 · Cited by 38,834 · see citing works
Chen, L.-C., Papandreou, G., Kokkinos, I., Murphy, K., & Yuille, A. L. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2017.2699184 · Cited by 22,392 · see citing works
SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation
Badrinarayanan, V., Kendall, A., & Cipolla, R. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2644615 · Cited by 19,631 · see citing works
Zhao, H., Shi, J., Qi, X., Wang, X., & Jia, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.660 · Cited by 16,438 · see citing works
TensorFlow: A system for large-scale machine learningPreprint
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al. (2016) · arXiv (Cornell University) · doi:10.5555/3026877.3026899 · Cited by 16,369 · see citing works
Representation Learning: A Review and New Perspectives
Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works
RefineNet: Multi-path Refinement Networks for High-Resolution Semantic Segmentation
Lin, G., Milan, A., Shen, C., & Reid, I. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.549 · Cited by 3,287 · cited 4× · see citing works
“For some large area defects, [21] and [25] can’t accurately locate defects.”
BackgroundInfluentialWhat is the best multi-stage architecture for object recognition?
Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153 · see citing works
Understanding Convolution for Semantic Segmentation
Wang, P., Chen, P., Yuan, Y., Liu, D., Huang, Z., Hou, X., & Cottrell, G. W. (2018) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv.2018.00163 · Cited by 2,127 · see citing works
Fast Feature Pyramids for Object Detection
Dollár, P., Appel, R., Belongie, S., & Perona, P. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2014.2300479 · Cited by 1,941 · see citing works
Yu, F., Koltun, V., & Funkhouser, T. A. (2017) · 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.2017.75 · Cited by 1,925 · see citing works
Understanding deep image representations by inverting them
Mahendran, A., & Vedaldi, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7299155 · Cited by 1,827 · see citing works
ENet: A Deep Neural Network Architecture for Real-Time Semantic Segmentation
Paszke, A., Chaurasia, A., Kim, S., & Culurciello, E. (2016) · arXiv · doi:10.48550/arxiv.1606.02147 · Cited by 1,826 · see citing works
Large Kernel Matters — Improve Semantic Segmentation by Global Convolutional Network
Peng, C., Zhang, X., Yu, G., Luo, G., & Sun, J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.189 · Cited by 1,750 · see citing works
Yu, F., Wang, D., Shelhamer, E., & Darrell, T. (2018) · 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.2018.00255 · Cited by 1,592 · see citing works
An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features
He, Y., Song, K., Meng, Q., & Yan, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2019.2915404 · Cited by 1,341 · see citing works
Self-citationVisual saliency based on multiscale deep features
Li, G., & Yu, Y. (2015) · 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.2015.7299184 · Cited by 1,298 · see citing works
Feature Pyramid and Hierarchical Boosting Network for Pavement Crack Detection
Yang, F., Zhang, L., Yu, S., Prokhorov, D. V., Mei, X., & Ling, H. (2019) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2019.2910595 · Cited by 1,248 · see citing works
Saliency detection by multi-context deep learning
Zhao, R., Ouyang, W., Li, H., & Wang, X. (2015) · 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.2015.7298731 · Cited by 1,030 · 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
Self-citationChen, F., & Jahanshahi, M. R. (2017) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2017.2764844 · Cited by 933 · see citing works
Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection
Zhang, P., Wang, D., Lu, H., Wang, H., & Ruan, X. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.31 · Cited by 913 · see citing works
Object Detectors Emerge in Deep Scene CNNsPreprint
Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., & Torralba, A. B. (2014) · arXiv (Cornell University) · doi:10.48550/arxiv.1412.6856 · Cited by 823 · see citing works
Deeply Supervised Salient Object Detection with Short Connections
Hou, Q., Cheng, M., Hu, X., Borji, A., Tu, Z., & Torr, P. H. S. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2815688 · Cited by 674 · see citing works
Salient Object Detection With Pyramid Attention and Salient Edges
Wang, W., Shuyang, Z., Shen, J., Hoi, S. C. H., & Borji, A. (2019) · 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.2019.00154 · Cited by 563 · see citing works
Learning Uncertain Convolutional Features for Accurate Saliency Detection
Zhang, P., Wang, D., Lu, H., Wang, H., & Yin, B. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.32 · Cited by 494 · 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
Object Detectors Emerge in Deep Scene CNNsPreprint
Zhou, B., Khosla, A., Lapedriza, À., Oliva, A., & Torralba, A. B. (2015) · DSpace@MIT (Massachusetts Institute of Technology) · Cited by 465 · see citing works
Surface defect saliency of magnetic tile
Huang, Y., Qiu, C., & Yuan, K. (2018) · The Visual Computer · doi:10.1007/s00371-018-1588-5 · Cited by 460 · see citing works
Convolutional Networks Can Learn to Generate Affinity Graphs for Image Segmentation
Turaga, S. C., Murray, J. F., Jain, V., Roth, F., Helmstaedter, M., Briggman, K., Denk, W., & Seung, H. S. (2010) · Neural Computation · doi:10.1162/neco.2009.10-08-881 · Cited by 376 · see citing works
A Mutual Learning Method for Salient Object Detection With Intertwined Multi-Supervision
Wu, R., Feng, M., Guan, W., Wang, D., Lu, H., & Ding, E. (2019) · 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.2019.00834 · Cited by 272 · see citing works
Saliency-Based Defect Detection in Industrial Images by Using Phase Spectrum
Bai, X., Fang, Y., Lin, W., Wang, L., & Ju, B. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2359416 · Cited by 225 · see citing works
Zhang, H., Jin, X., Wu, Q. M. J., et al. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2803830 · Cited by 193 · see citing works
Defect Detection in Solar Modules Using ICA Basis Images
Tsai, D., Wu, S.-C., & Chiu, W. (2012) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2012.2209663 · Cited by 189 · see citing works
Salient Object Detection with Recurrent Fully Convolutional Networks
Wang, L., Wang, L., Lu, H., Zhang, P., & Ruan, X. (2018) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2018.2846598 · Cited by 188 · see citing works
Yang, H. S., Chen, Y., Song, K., & Yin, Z. (2019) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2018.2886031 · Cited by 179 · see citing works
Self-citationGeneralized Completed Local Binary Patterns for Time-Efficient Steel Surface Defect Classification
Luo, Q., Sun, Y., Li, P., Simpson, O., Tian, L., & He, Y. (2019) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2852918 · Cited by 178 · see citing works
Self-citationHe, Y., Song, K., Dong, H., & Yan, Y. (2019) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2019.06.020 · Cited by 172 · see citing works
Self-citationA CNN-Based Defect Inspection Method for Catenary Split Pins in High-Speed Railway
Zhong, J., Liu, Z., Han, Z., Han, Y., & Zhang, W. (2018) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2018.2871353 · Cited by 160 · see citing works
TEXEMS: Texture Exemplars for Defect Detection on Random Textured Surfaces
Xie, X., & Mirmehdi, M. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1038 · Cited by 159 · see citing works
A Simple Guidance Template-Based Defect Detection Method for Strip Steel Surfaces
Wang, H., Zhang, J., Tian, Y., Chen, H., Sun, H., & Liu, K. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2887145 · Cited by 153 · see citing works
A Contrast Adjustment Thresholding Method for Surface Defect Detection Based on Mesoscopy
Win, M., Bushroa, A. R., Hassan, M. A., Hilman, N. M., & Ide-Ektessabi, A. (2015) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2015.2417676 · Cited by 142 · see citing works
A Simplified Computer Vision System for Road Surface Inspection and Maintenance
Quintana, M. I., Torres, J., & Menéndez, J. M. (2015) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2015.2482222 · Cited by 141 · see citing works
Surface Defect Detection via Entity Sparsity Pursuit With Intrinsic Priors
Wang, J., Li, Q., Gan, J., Yu, H., & Yang, X. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2019.2917522 · Cited by 141 · see citing works
LIP: Local Importance-Based Pooling
Gao, Z., Wang, L., & Wu, G. (2019) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00345 · Cited by 138 · see citing works
Surface Defect Saliency of Magnetic Tile
Huang, Y., Qiu, C., Guo, Y., Wang, X., & Yuan, K. (2018) · 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) · doi:10.1109/coase.2018.8560423 · Cited by 117 · see citing works
Convolutional networks for voting-based anomaly classification in metal surface inspection
Natarajan, V., Hung, T.-Y., Vaikundam, S., & Chia, L.-T. (2017) · 2017 IEEE International Conference on Industrial Technology (ICIT) · doi:10.1109/icit.2017.7915495 · Cited by 79 · see citing works
Multi-scale pyramidal pooling network for generic steel defect classification
Masci, J., Meier, U., Fricout, G., & Schmidhuber, J. (2013) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2013.6706920 · Cited by 52 · see citing works
Weakly Supervised Learning for Industrial Optical Inspection
Wieler, M., Hahn, T., & Hamprecht, F. A. (2007) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.8086135 · Cited by 27 · see citing works
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A New Steel Defect Detection Algorithm Based on Deep Learning
Zhao, W., Chen, F., Huang, H., Li, D., & Cheng, W. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/5592878 · Cited by 259 · see citing works
“Therefore, the steel surface defect detection method based on deep learning is widely used in steel production, and more scholars begin to improve and perfect it [13, 14].”
MethodX-SDD: A New Benchmark for Hot Rolled Steel Strip Surface Defects Detection
Feng, X., Gao, X., & Luo, L. (2021) · Symmetry · doi:10.3390/sym13040706 · Cited by 158 · see citing works
“Many high-level studies have been conducted based on this dataset, for example, [32–36].”
BackgroundCi, J., Tan, H., Zhai, H., & Tang, X. (2024) · Remote Sensing · doi:10.3390/rs16162953 · Cited by 4 · see citing works
“Hongwen Dong et al. [26] proposed PGA-Net, which can detect pixel-level surface defects by pyramidal feature fusion and adding global contextual attention; Yingchao Zhang et al. [27] proposed a multilevel attention-based (MLAB) YOLOv3 UAV image road detection model with a model mAP value up to…”
Method
- Authors
- Dong, H., Song, K., He, Y., Xu, J., Yan, Y., & Meng, Q.
- Affiliations
- Northeastern University (CN)
- Loughborough University (GB)
- Venue
- IEEE Transactions on Industrial Informatics
- Published
- 2020-12
- Volume
- 16
- Issue
- 12
- Pages
- 7448-7458
- ISSN
- 1551-3203 (ISSN-L)
- 1941-0050
- OpenAlex
- W2994615081
- Semantic Scholar
- d81421695d5d429a47eefe266986d197d7b313f2
- Cited by
- 559
- References
- 53
- Citations by year
- 2026: 76 · 2025: 108 · 2024: 104 · 2023: 112 · 2022: 78 · 2021: 53 · 2020: 28
- FWCI
- 29.32
- Type
- journal article
- Language
- en
- Open access
- green
- figshare.com
- Licence
- Locations
- IEEE Transactions on Industrial Informatics (publishedVersion)
- Figshare (submittedVersion, cc-by)
- Funders
- National Natural Science Foundation of China (51805078, 51374063)
- National Key Research and Development Program of China Stem Cell and Translational Research (2017YFB0304200)
- Fundamental Research Funds for the Central Universities (N170304014)
- 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 › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
- Keywords
- surface defect detection, automated visual inspection, pixel-level segmentation, global context attention, multi-scale feature fusion, dense skip connections, boundary refinement, industrial inspection
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