Cited by 137Industrial Vision Systems and Defect DetectionNon-Destructive Testing TechniquesInfrastructure Maintenance and Monitoring

The DOI matches its Crossref record; 30 of the 30 references it cites were found in a registry or an index.

Deep-Learning-Based Small Surface Defect Detection via an Exaggerated Local Variation-Based Generative Adversarial Network

Lian, Jian, Jia, Weikuan, Zareapoor, Masoumeh, Zheng, Yuanjie, Luo, Rong, Jain, Deepak Kumar, Kumar, Neeraj·2020·IEEE Transactions on Industrial Informatics

DOI 10.1109/tii.2019.2945403

Open this paper in CoChat — free account

Abstract

Surface detection of small defects plays a vital role in manufacturing and has attracted broad interest. It remains challenging primarily due to the small size of the defect relative to the large surface and the rare occurrence of defects. To address this problem, in this article we propose a novel machine vision approach for automatically identifying the tiny flaws that may appear in a single image. First, the presented defect exaggeration approach produces both the flawless image and the corresponding exaggerated version of the defect by taking the variations in the image as regularization terms. Second, a generative adversarial network (GAN) in conjunction with a convolutional neural network (CNN) is proposed to guarantee the accuracy of tiny surface defect detection by producing exaggerated defect image samples. Furthermore, the limited dataset of the training samples for defect detection is enlarged by exploiting the GAN technique with the variation exaggerated images. To evaluate the performance of our proposed method, we conduct comparison experiments between the state-of-the-art techniques with and without the proposed algorithm as well as comparison experiments between the state-of-the-art techniques and our method. The experimental results on different types of surface image samples demonstrate that the proposed method can significantly improve the performance of the state-of-the-art approaches while achieving a defect detection accuracy of 99.2%.

Integrity signals

Registry

Crossref · 10.1109/tii.2019.2945403

Cites

30 of 30 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

This paper’s full text isn’t openly available, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Fast R-CNN
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Generative Adversarial Nets
Labaca-Castro, R. (2023) · Springer eBooks · doi:10.1007/978-3-658-40442-0_9 · Cited by 21,119
GAN（Generative Adversarial Nets）
淳司, 柴. (2017) · Journal of Japan Society for Fuzzy Theory and Intelligent Informatics · doi:10.3156/jsoft.29.5_177_2 · Cited by 20,507
Multiresolution gray-scale and rotation invariant texture classification with local binary patterns
Ojala, T., Pietikainen, M., & Maenpaa, T. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2002.1017623 · Cited by 15,398
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
Ledig, C., Theis, L., Huszar, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.19 · Cited by 12,696
Generative Adversarial Networks: An Overview
Creswell, A., White, T., Dumoulin, V., Arulkumaran, K., Sengupta, B., & Bharath, A. A. (2018) · IEEE Signal Processing Magazine · doi:10.1109/msp.2017.2765202 · Cited by 4,540
High Accuracy Optical Flow Estimation Based on a Theory for Warping
Brox, T., Bruhn, A., Papenberg, N., & Weickert, J. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-24673-2_3 · Cited by 2,634
Automatic thresholding for defect detection
Ng, H. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2006.03.009 · Cited by 554
Generative Adversarial Networks
Salvaris, M., Dean, D., & Tok, W. H. (2018) · Apress eBooks · doi:10.1007/978-1-4842-3679-6_8 · Cited by 411
PairedCycleGAN: Asymmetric Style Transfer for Applying and Removing Makeup
Chang, H., Lu, J., Yu, F., & Finkelstein, A. (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.2018.00012 · Cited by 297

Show all 30 references

Research works printed but not linked: 1

wang, 2018, Development and improvement of deep learning based automated defect detection for sewer pipe inspection using faster R-CNNC, Workshop of the European Group for Intelligent Computing in Engineering, 171

Sorted by citations

Image-Based Surface Defect Detection Using Deep Learning: A Review
Bhatt, P. M., Malhan, R. K., Rajendran, P. S., Shah, B. C., Thakar, S., Yoon, Y. J., & Gupta, S. K. (2021) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4049535 · Cited by 392
A Comprehensive Review of Convolutional Neural Networks for Defect Detection in Industrial Applications
Khanam, R., Hussain, M., Hill, R. C., & Allen, P. (2024) · IEEE Access · doi:10.1109/access.2024.3425166 · Cited by 296
Deep CNN-based visual defect detection: Survey of current literature
Jha, S. B., & Babiceanu, R. F. (2023) · Computers in Industry · doi:10.1016/j.compind.2023.103911 · Cited by 254
Steel Surface Defect Recognition: A Survey
Wen, X., Shan, J., He, Y., & Song, K. (2022) · Coatings · doi:10.3390/coatings13010017 · Cited by 210
Intelligent disassembly of electric-vehicle batteries: a forward-looking overview
Meng, K., Xu, G., Peng, X., Youcef‐Toumi, K., & Li, J. (2022) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2022.106207 · Cited by 188
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 170
Automated Visual Defect Classification for Flat Steel Surface: A Survey
Luo, Q., Fang, X., Su, J., Zhou, J., Zhou, B., Yang, C., Liu, L., Gui, W., & Tian, L. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3030167 · Cited by 148
Unsupervised Anomaly Detection for Surface Defects With Dual-Siamese Network
Tao, X., Zhang, D., Ma, W., Hou, Z., Lu, Z., & Adak, C. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3142326 · Cited by 116
A Generative Adversarial Network Based Deep Learning Method for Low-Quality Defect Image Reconstruction and Recognition
Gao, Y., Gao, L., & Li, X. (2020) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2020.3008703 · Cited by 112
A Systematic Review on Deep Learning with CNNs Applied to Surface Defect Detection
Cumbajin, E., Rodrigues, N., Costa, P., Miragaia, R., Frazao, L. A. L., Costa, N., Fernández‐Caballero, A., Carneiro, J., Buruberri, L. H., & Pereira, A. M. D. J. (2023) · Journal of Imaging · doi:10.3390/jimaging9100193 · Cited by 122
Auto-Annotated Deep Segmentation for Surface Defect Detection
Tsai, D., Fan, S. S., & Chou, Y.-H. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3087826 · Cited by 96
A Novel State-of-Health Estimation for Lithium-Ion Battery via Unscented Kalman Filter and Improved Unscented Particle Filter
Zhu, F., & Fu, J. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3102990 · Cited by 88
A supervised approach for automated surface defect detection in ceramic tile quality control
Lu, Q., Lin, J., Luo, L., Zhang, Y., & Zhu, W. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101692 · Cited by 85
Few-Shot Steel Surface Defect Detection
Wang, H., Li, Z., & Wang, H. (2021) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2021.3128208 · Cited by 92
Automatic defogging, deblurring, and real-time segmentation system for sewer pipeline defects
Ma, D., Fang, H., Wang, N., Zheng, H., Dong, J., & Hu, H. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104595 · Cited by 75
A New Cycle-consistent Adversarial Networks With Attention Mechanism for Surface Defect Classification With Small Samples
Wen, L., Wang, Y., & Li, X. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3168432 · Cited by 67
AIGC-empowered smart manufacturing: Prospects and challenges
Leng, J., Zheng, K., Li, R. C., Chen, C., Wang, B., Liu, Q., Chen, X., & Shen, W. (2025) · Robotics and Computer-Integrated Manufacturing · doi:10.1016/j.rcim.2025.103076 · Cited by 74
Attention-Guided Multitask Learning for Surface Defect Identification
Sampath, V., Maurtua, I., Martín, J. J. A., Rivera, A., Molina, J., & Gutierrez, A. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3234030 · Cited by 69
A Review on Industrial Surface Defect Detection Based on Deep Learning Technology
Qi, S., Yang, J., & Zhong, Z. (2020) · International Conference on Machine Learning and Machine Intelligence (MLMI) · doi:10.1145/3426826.3426832 · Cited by 78
Intelligent Small Sample Defect Detection of Water Walls in Power Plants Using Novel Deep Learning Integrating Deep Convolutional GAN
Geng, Z., Shi, C., & Han, Y. M. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3159817 · Cited by 64
Global Prior Transformer Network in Intelligent Borescope Inspection for Surface Damage Detection of Aeroengine Blade
Shang, H., Wu, J., Sun, C., Liu, J., Chen, X., & Yan, R. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3222300 · Cited by 63
Partial Discharge Data Augmentation Based on Improved Wasserstein Generative Adversarial Network With Gradient Penalty
Zhu, G., Zhou, K., Lu, L., Fu, Y., Liu, Z., & Yang, X. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3197839 · Cited by 62
Mask2Defect: A Prior Knowledge-Based Data Augmentation Method for Metal Surface Defect Inspection
Yang, B., Liu, Z., Duan, G., & Tan, J. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3126098 · Cited by 61
Generative adversarial network-based semi-supervised learning for real-time risk warning of process industries
He, R., Li, X., Chen, G., Chen, G., & Liu, Y. (2020) · Expert Systems with Applications · doi:10.1016/j.eswa.2020.113244 · Cited by 59
A Survey of Defect Detection Applications Based on Generative Adversarial Networks
He, X., Chang, Z., Zhang, L., Xu, H., Chen, H., & Luo, Z. (2022) · IEEE Access · doi:10.1109/access.2022.3217227 · Cited by 57
ALADA: A lite automatic data augmentation framework for industrial defect detection
Wang, Y., Chung, S., Khan, W. A., Wang, T., & Xu, D. J. (2023) · Advanced Engineering Informatics · doi:10.1016/j.aei.2023.102205 · Cited by 52
CASDD: Automatic Surface Defect Detection Using a Complementary Adversarial Network
Tian, S., Huang, P., Ma, H., Wang, J., Zhou, X., Zhang, S., Zhou, J., Huang, R., & Li, Y. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3202179 · Cited by 46
A Survey of Vision-Based Methods for Surface Defects’ Detection and Classification in Steel Products
Ibrahim, A. A. M. S., & Tapamo, J. (2024) · Informatics · doi:10.3390/informatics11020025 · Cited by 57
Defect attention template generation cycleGAN for weakly supervised surface defect segmentation
Niu, S., Li, B., Wang, X., He, S., & Peng, Y. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108396 · Cited by 42
Erasing-inpainting-based data augmentation using denoising diffusion probabilistic models with limited samples for generalized surface defect inspection
Tao, H. (2023) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2023.111082 · Cited by 39
An Adaptive Defect-Aware Attention Network for Accurate PCB-Defect Detection
Liu, X. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3488158 · Cited by 37
TAS 2 -Net: Triple-Attention Semantic Segmentation Network for Small Surface Defect Detection
Liu, T., & He, Z. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3142023 · Cited by 38
AI-enabled defect detection in industrial products: A comprehensive survey, key insights and future research challenges
Nahar, L., Awrangjeb, M., & Islam, M. S. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.104067 · Cited by 43
I-YOLO: a novel single-stage framework for small object detection
Tong, K., & Wu, Y. (2024) · The Visual Computer · doi:10.1007/s00371-024-03284-8 · Cited by 35
A MFL Mechanism-Based Self-Supervised Method for Defect Detection With Limited Labeled Samples
Zhao, H. M., Liu, J., Tang, J., Shen, X., Lu, S., & Wang, Q. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3212041 · Cited by 35
Subtle Defect Detection Network: More accurately detect subtle defects on the Printed Circuit Board surface
Zhu, L., Wang, S., Chen, M., Zhu, Y., Xing, K., & Shen, A. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110765 · Cited by 34
Application of Lah transform for security and privacy of data through information hiding in telecommunication
Ghosal, S. K., Mukhopadhyay, S., Hossain, S., & Sarkar, R. (2020) · Transactions on Emerging Telecommunications Technologies · doi:10.1002/ett.3984 · Cited by 36
Research on Financial Investment Decision Based on Artificial Intelligence Algorithm
Ren, J. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3104038 · Cited by 42
An Efficient Anchor-Free Defect Detector With Dynamic Receptive Field and Task Alignment
Zuo, F., Liu, J., Fu, M., Wang, L., & Zhao, Z. M. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3371982 · Cited by 30
Uncertainty-aware and dynamically-mixed pseudo-labels for semi-supervised defect segmentation
Sime, D. M., Wang, G., Zeng, Z. P., & Peng, B. (2023) · Computers in Industry · doi:10.1016/j.compind.2023.103995 · Cited by 28
Implementation of Machine Vision based Quality Inspection in Production: An Approach for the Accelerated Execution of Case Studies
Reichenstein, T., Raffin, T., Sand, C., & Franke, J. E. (2022) · Procedia CIRP · doi:10.1016/j.procir.2022.09.058 · Cited by 24
Abnormal event detection using adversarial predictive coding for motion and appearance
Yu, J., Kim, J.-G., Gwak, J., Lee, B., & Jeon, M. (2021) · Information Sciences · doi:10.1016/j.ins.2021.11.001 · Cited by 22
IA-Mask R-CNN: Improved Anchor Design Mask R-CNN for Surface Defect Detection of Automotive Engine Parts
Zhu, H., Wang, Y., & Fan, J. (2022) · Applied Sciences · doi:10.3390/app12136633 · Cited by 25
A Novel Weakly Supervised Ensemble Learning Framework for Automated Pixel-Wise Industry Anomaly Detection
Mei, S., Cheng, J., He, X., Hu, H., & Wen, G. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3131908 · Cited by 20
Virtual sample generation method based on generative adversarial fuzzy neural network
Cui, C., Tang, J., Xia, H., Qiao, J., & Yu, W. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-08104-5 · Cited by 19
RADDA-Net: Residual attention-based dual discriminator adversarial network for surface defect detection
Tian, S., Ma, H., Huang, P., Wang, X., Li, T., & Huang, R. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108887 · Cited by 18
Multi-scale sensing and multi-dimensional feature enhancement for surface defect detection of hot-rolled steel strip
Li, X., Xu, C., Li, J., Zhou, X., & Li, Y. (2024) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2024.2408441 · Cited by 19
Integrating Deformable Convolution and Pyramid Network in Cascade R-CNN for Fabric Defect Detection
Li, H., Zhang, H., Liu, L., Zhong, H., Wang, Y., & Wu, Q. M. J. (2020) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc42975.2020.9282875 · Cited by 18
A Siamese Network Utilizing Image Structural Differences For Cross-Category Defect Detection
Luan, C., Cui, R., Сун, Л., & Lin, Z. (2020) · Proceedings - International Conference on Image Processing · doi:10.1109/icip40778.2020.9191128 · Cited by 17
The Prospect of Artificial Intelligence-Based Wood Surface Inspection: A Review
Yi, L. P., Akbar, M. F., Wahab, M. N. A., Rosdi, B. A., Fauthan, M. ‘., & Shrifan, N. H. M. M. (2024) · IEEE Access · doi:10.1109/access.2024.3412928 · Cited by 23
Computer vision in manufacturing: a bibliometric analysis and future research propositions
Sharma, H., Kumar, H., Gupta, A., & Shah, M. A. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-11907-y · Cited by 17
A light-weight defect detection model for capacitor appearance based on the Yolov5
Xu, L., Xu, X., Xia, Q., Yao, Y., & Jiang, Z. (2024) · Measurement · doi:10.1016/j.measurement.2024.114717 · Cited by 16
Fabric defects identification for textile industry with a deep learning approach
Güder, O., Işık, Ş., & Anagün, Y. (2024) · Journal of the Textile Institute · doi:10.1080/00405000.2024.2383799 · Cited by 16
A Novel Depth-Connected Region-Based Convolutional Neural Network for Small Defect Detection in Additive Manufacturing
Wang, Y., Wang, Z., Liu, W., Zeng, N., Lauria, S., Prieto, C., Sikström, F., Yu, H. L., & Liu, X. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10397-8 · Cited by 16
A One‐Stage Deep Learning Model for Industrial Defect Detection
Li, Z., Wei, X., Hassaballah, M., & Jiang, X. (2023) · Advanced Theory and Simulations · doi:10.1002/adts.202200853 · Cited by 15
An adaptive loss weighting multi-task network with attention-guide proposal generation for small size defect inspection
Wu, H., Li, B., Tian, L., Feng, J., & Dong, C. (2023) · The Visual Computer · doi:10.1007/s00371-023-02809-x · Cited by 14
Automated corner grading of trading cards: Defect identification and confidence calibration through deep learning
Nahar, L., Islam, M. S., Awrangjeb, M., & Verhoeve, R. (2024) · Computers in Industry · doi:10.1016/j.compind.2024.104187 · Cited by 19
SPAMI: A cognitive spam protector for advertisement malicious images
Makkar, A., Kumar, N., Zomaya, A. Y., & Dhiman, S. (2020) · Information Sciences · doi:10.1016/j.ins.2020.05.113 · Cited by 14
Self-citation
Research on surface defect intelligent detection technology of non-woven fabric based on support vector machine
Huang, Y., Yi, M., Yang, W., & Yang, M. (2022) · IEEE International Conference on Electrical Engineering, Big Data and Algorithms (EEBDA) · doi:10.1109/eebda53927.2022.9744952 · Cited by 13
A Highly Efficient and Lightweight Detection Method for Steel Surface Defect
Xu, C., Li, J., & Li, X. (2024) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-024-01084-7 · Cited by 12
Intelligent IoT framework with GAN‐synthesized images for enhanced defect detection in manufacturing
Aramkul, S., & Sugunnasil, P. (2023) · Computational Intelligence · doi:10.1111/coin.12619 · Cited by 11
G-Anomaly: A Pyramid Graph Transformer-Based Vision-Language Model for General Industrial Anomaly Detection
Li, J., Wen, S., & Fang, B. (2025) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2025.3552986 · Cited by 11
Efficient Deep Q-Learning for Industrial Equipment Calibration in Elevator Manufacturing
Evangelidis, A., Dimitriou, N., Charalampous, P., Mastos, T. D., & Tzovaras, D. K. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3417254 · Cited by 10
CLIP-Vision Guided Few-Shot Metal Surface Defect Recognition
Wang, T., Li, Z., Xu, Y., Zhai, Y., Xing, X., Guo, K., Coscia, P., Genovese, A., Piuri, V., & Scotti, F. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3547353 · Cited by 11
Neural-Architecture-Search-Based Multiobjective Cognitive Automation System
Wang, E. K., Xu, P., Chen, C., & Kumar, N. (2020) · IEEE Systems Journal · doi:10.1109/jsyst.2020.3002428 · Cited by 10
Self-citation
RoIA: Region of Interest Attention Network for Surface Defect Detection
Liu, T., Cao, G., He, Z., & Xie, S. (2023) · IEEE Transactions on Semiconductor Manufacturing · doi:10.1109/tsm.2023.3265987 · Cited by 10
Morphological Image Analysis and Feature Extraction for Reasoning with AI-Based Defect Detection and Classification Models
Zhang, J., Cosma, G., Bugby, S. L., Finke, A., & Watkins, J. (2023) · IEEE Symposium Series on Computational Intelligence (SSCI) · doi:10.1109/ssci52147.2023.10371832 · Cited by 10
Multi-level semantic information guided image generation for few-shot steel surface defect classification
Hao, L., Shen, P., Pan, Z., & Xu, Y. D. (2023) · Frontiers in Physics · doi:10.3389/fphy.2023.1208781 · Cited by 9
Weakly Supervised End-to-End Learning for Inspection on Multidirectional Integrated Circuit Markings in Surface Mount Technology
Chen, Z., Zuo, L., Guo, F., Zhang, C., & Liu, Y. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2023.3301065 · Cited by 9
An online automatic carbide insert high-resolution surface defect detection system based on template-guided model
Zhang, W., Hu, Y., Shan, H., & Liu, E. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122089 · Cited by 9
Augmented classification for electrical coil winding defects
Farnsworth, M., Tiwari, D., Zhang, Z., Jewell, G. W., & Tiwari, A. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-08671-w · Cited by 9
RBD-Net: robust breakage detection algorithm for industrial leather
Luo, R., Chen, R., Jia, F., Lin, B., Liu, J., Sun, Y., Yang, X., & Jia, W. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-01962-9 · Cited by 9
Self-citation
Uncalibrated multi-view multiple humans association and 3D pose estimation by adversarial learning
Ershadi-Nasab, S., Kasaei, S., & Sanaei, E. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-020-09733-5 · Cited by 10
FBSM: Foveabox-based boundary-aware segmentation method for green apples in natural orchards
Jia, W., Wang, Z., Zhao, R., Ji, Z., Yin, X., & Liu, G. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125426 · Cited by 8
Self-citation
Touch: A New Paradigm Based on Machine Tactile Sensation for Surface Microdefect Detection
Xi, Z., Li, X., Gao, L., & Gao, Y. (2025) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2025.3638047 · Cited by 8
Semi-supervised auxiliary learning for surface defect detection and segmentation of injection-molded products from small image datasets
Yu, Z., Zhang, Y., Zhang, S., Yi, G., & Qiu, L. (2024) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-024-13275-7 · Cited by 10
AtomGAN: unsupervised deep learning for fast and accurate defect detection of 2D materials at the atomic scale
Cheng, D., Sha, W., Zuo, X., Li, S., Yin, Z., Lang, Y., Tang, S., & Cao, Y. (2023) · Science China Information Sciences · doi:10.1007/s11432-022-3757-x · Cited by 8
A novel vision-based defect detection method for hot-rolled steel strips via multi-branch network
Yang, L., Li, X., & Liu, Y. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15753-8 · Cited by 8
Purposive Data Augmentation Strategy and Lightweight Classification Model for Small Sample Industrial Defect Dataset
Lin, L., Zhao, S., Zhang, Y., Wen, A., Zhang, S., Yan, J., Wang, Y., & Zhou, Y. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3404053 · Cited by 7
Intelligent modeling and detection in grinding: a review of advances, challenges, and prospects
Zhang, Z., Li, J., Lu, L., Wang, Z., Liang, X., & Zhang, Y. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-15711-8 · Cited by 7
Incorporating long-tail data in complex backgrounds for visual surface defect detection in PCBs
Zhu, L., Wang, S., Chen, M., Shen, A., & Li, X. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-024-01554-5 · Cited by 7
Research on Detection Method of Sheet Surface Defects Based on Machine Vision
Huang, Y., Yi, M., Yang, W., & Yang, M. (2021) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/632/5/052085 · Cited by 7
Multi-defect detection for magnetic tile based on SE-U-Net
Cao, X., Yao, B., Chen, B., & Wang, Y. (2020) · IEEE International Symposium on Product Compliance Engineering - Asia (ISPCE-ASIA) · doi:10.1109/ispce-cn51288.2020.9321855 · Cited by 7
Anomaly localization in regular textures based on deep convolutional generative adversarial networks
Öz, M. A. N., Mercımek, M., & Kaymakçı, Ö. T. (2021) · Applied Intelligence · doi:10.1007/s10489-021-02475-3 · Cited by 7
Research on the Impact of Investment Efficiency of China’s Commercial Economy on Industrial Structure Adjustment Based on Cloud Computing
Huang, W. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3108141 · Cited by 7
Inter-layer explainable variational autoencoder model for multivariate time series anomaly detection
Zhang, X., Wang, G., Chen, Y., Yang, W., & Wang, G. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.111585 · Cited by 8
Cylinder Liner Defect Detection and Classification based on Deep Learning
Gao, C., Hao, F., Song, J., Chen, R., Wang, F., & Liu, B. (2022) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2022.0130818 · Cited by 6
GAN-based Data Augmentation for Metal Surface Defect Detection Using Convolutional Neural Networks
Tseng, L.-S., Wu, C., Chen, Y. H., & Tsai, C.-H. (2023) · International Symposium on Computer, Consumer and Control · doi:10.1109/is3c57901.2023.00029 · Cited by 6
Defect detection of photovoltaic glass based on level set map
Dong, S., Chen, C., Liang, Y., Zou, K., & Liu, G. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07005-x · Cited by 6
Real-Time Optical Detection of Artificial Coating Defects in PBF-LB/P Using a Low-Cost Camera Solution and Convolutional Neural Networks
Klamert, V., Achsel, T., Toker, E., Bublin, M., & Otto, A. (2023) · Applied Sciences · doi:10.3390/app132011273 · Cited by 5
An efficient multi-scale feature enhancement network for industrial surface defect detection
Chen, J., Zha, H., Zhang, X., Guo, R., & Wu, J. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adb32a · Cited by 5
Defect Recognition for Vehicle Cable Terminals in EMUs Using Data Augmentation and Improved ResNet Model
Jiang, W., Zhou, L., Ma, H., Ding, W., Xu, K., & Wang, D. (2025) · IEEE Transactions on Transportation Electrification · doi:10.1109/tte.2025.3568928 · Cited by 5
A nested autoencoder approach to automated defect inspection on textured surfaces
Öz, M. A. N., Kaymakçı, Ö. T., & Mercımek, M. (2021) · International Journal of Applied Mathematics and Computer Science · doi:10.34768/amcs-2021-0035 · Cited by 5
Research on Detection Method of Coating Defects Based on Machine Vision
Hui, Z., Lv, Y., Sha, J., Peng, R., Chen, Z., & Wang, G. (2021) · IEEE International Conference on Artificial Intelligence and Computer Applications (ICAICA) · doi:10.1109/icaica52286.2021.9498238 · Cited by 5
SCFNet: Lightweight Steel Defect Detection Network Based on Spatial Channel Reorganization and Weighted Jump Fusion
Li, H., Yi, Z., Mei, L., Duan, J., Sun, K., Li, M., Yang, W., & Wang, Y. (2024) · Processes · doi:10.3390/pr12050931 · Cited by 5
Long-Tailed Defects Classification Based on Probabilistic Aggregation Network for Light-Emitting Diode Packaging Process
Chen, J., Yin, H., Qin, Y., & Jiang, W. (2024) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2024.3413982 · Cited by 4
Automated Defective Ceramic Tiles Classification using Image Processing Techniques
Ibrahim, S., Vauxhall, N. A. E. M., Mangshor, N. N. A., Fadzil, A. F. A., & Ghani, N. A. M. (2023) · Journal of Advanced Research in Applied Sciences and Engineering Technology · doi:10.37934/araset.32.3.355365 · Cited by 4
A global centralized magnetic flux leakage small defect detection network
Chen, Y., Lang, X., & Liu, M. (2024) · Engineering Research Express · doi:10.1088/2631-8695/ad2ab6 · Cited by 4
Physically-based data augmentation for deep learning-enabled automated visual inspection of scratches
Wang, P., Wang, W., & Wang, Y. (2024) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case59546.2024.10711456 · Cited by 4
An Automatic Defect Detection System for Synthetic Shuttlecocks Using Transformer Model
Lin, C.-S., & Hsieh, H.-Y. (2022) · IEEE Access · doi:10.1109/access.2022.3165224 · Cited by 4

Authors: Lian, J., Jia, W., Zareapoor, M., Zheng, Y., Luo, R., Jain, D. K., & Kumar, N.
Affiliations: Shandong Normal University (CN)
Shanghai Jiao Tong University (CN)
Qilu University of Technology (CN)
Chongqing University of Posts and Telecommunications (CN)
Thapar Institute of Engineering & Technology (IN)
ORCID: Lian, Jian
Jia, Weikuan
Zareapoor, Masoumeh
Zheng, Yuanjie
Luo, Rong
Jain, Deepak Kumar
Kumar, Neeraj
Venue: IEEE Transactions on Industrial Informatics
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Published: 2020-02
Volume: 16
Issue: 2
Pages: 1343-1351
ISSN: 1551-3203 (ISSN-L)
1941-0050
DOI: 10.1109/tii.2019.2945403
OpenAlex: W2978742590
Cited by: 137
References: 30
Citations by year: 2026: 9 · 2025: 20 · 2024: 27 · 2023: 23 · 2022: 24 · 2021: 19 · 2020: 14
FWCI: 10.71
Type: journal article
Language: en
Open access: closed
Licence: ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)
doi.org/10.15223/policy-029 (stm-asf)
doi.org/10.15223/policy-037 (stm-asf)
Locations: IEEE Transactions on Industrial Informatics (publishedVersion)
Funders: National Natural Science Foundation of China (81871508, 61572300, 61773246)
Focus on Research and Development Plan in Shandong Province (2019GNC106115)
Taishan Scholar Program of Shandong Province of China (TSHW201502038)
China Postdoctoral Science Foundation (2018M630797)
Topics: Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
Physical Sciences › Engineering › Mechanical Engineering › Non-Destructive Testing Techniques
Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
Keywords: generative adversarial networks, data augmentation, convolutional neural networks, machine vision, manufacturing quality inspection

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

Deep-Learning-Based Small Surface Defect Detection via an Exaggerated Local Variation-Based Generative Adversarial Network

Abstract

Overview

Integrity signals

Verification

Claims the paper makes

Claims other papers make about this one

Citations

Cites

Cited by

Related

Metadata

Record

About these checks