Recent advances in surface defect inspection of industrial products using deep learning techniques

Deep Residual Learning for Image Recognition

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Very Deep Convolutional Networks for Large-Scale Image Recognition

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ImageNet classification with deep convolutional neural networks

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Deep learning

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Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

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ImageNet: A large-scale hierarchical image database

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Support-vector networks

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You Only Look Once: Unified, Real-Time Object Detection

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Densely Connected Convolutional Networks

Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.243 · Cited by 48,566

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

Steel Surface Defect Recognition: A Survey

Wen, X., Shan, J., He, Y., & Song, K. (2022) · Coatings · doi:10.3390/coatings13010017 · Cited by 210

Deep Learning for Automated Visual Inspection in Manufacturing and Maintenance: A Survey of Open- Access Papers

Hütten, N., Gomes, M. A., Hölken, F., Andricevic, K., Meyes, R., & Meisen, T. (2024) · Applied System Innovation · doi:10.3390/asi7010011 · Cited by 211

A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

Cui, Y., Liu, Z., & Lian, S. (2023) · IEEE Access · doi:10.1109/access.2023.3282993 · Cited by 185

Deep Learning for Automatic Vision-Based Recognition of Industrial Surface Defects: A Survey

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 174

Deep Learning and Computer Vision Techniques for Enhanced Quality Control in Manufacturing Processes

Islam, M. R., Zamil, M. Z. H., Rayed, M. E., Kabir, M. M., Mridha, M. F., Nishimura, S., & Shin, J. (2024) · IEEE Access · doi:10.1109/access.2024.3453664 · Cited by 171

Anomaly detection for industrial quality assurance: A comparative evaluation of unsupervised deep learning models

Zipfel, J., Verworner, F., Fischer, M., Wieland, U., Kraus, M., & Zschech, P. (2023) · Computers & Industrial Engineering · doi:10.1016/j.cie.2023.109045 · Cited by 134

Computer vision and deep learning-based approaches for detection of food nutrients/nutrition: New insights and advances

Kaushal, S., Tammineni, D. K., Rana, P., Sharma, M., Sridhar, K., & Chen, H. (2024) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2024.104408 · Cited by 111

A new lightweight deep neural network for surface scratch detection

Li, W., Zhang, L., Wu, C., Cui, Z., & Niu, C. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-10335-8 · Cited by 97

Traffic sign detection based on improved faster R-CNN for autonomous driving

Li, X., Xie, Z., Deng, X. F., Wu, Y., & Pi, Y. (2022) · The Journal of Supercomputing · doi:10.1007/s11227-021-04230-4 · Cited by 85

Depth-wise Squeeze and Excitation Block-based Efficient-Unet model for surface defect detection

Üzen, H., Türkoğlu, M., Aslan, M., & Hanbay, D. (2022) · The Visual Computer · doi:10.1007/s00371-022-02442-0 · Cited by 70

Coating Defects of Lithium-Ion Battery Electrodes and Their Inline Detection and Tracking

Schoo, A., Moschner, R., Hülsmann, J., & Kwade, A. (2023) · Batteries · doi:10.3390/batteries9020111 · Cited by 68

MD-YOLO: Surface Defect Detector for Industrial Complex Environments

Zheng, H., Chen, X., Cheng, H., Du, Y., & Jiang, Z. (2024) · Optics and Lasers in Engineering · doi:10.1016/j.optlaseng.2024.108170 · Cited by 68

SFW-YOLO: A lightweight multi-scale dynamic attention network for weld defect detection in steel bridge inspection

Luo, Y., Ling, J., Wang, J., Zhang, H., Chen, F., Xiao, X., & Lu, N. (2025) · Measurement · doi:10.1016/j.measurement.2025.117608 · Cited by 70

A Review on Machine and Deep Learning for Semiconductor Defect Classification in Scanning Electron Microscope Images

de la Rosa, F. L., Sánchez-Reolid, R., Gómez-Sirvent, J. L., Morales, R., & Fernández‐Caballero, A. (2021) · Applied Sciences · doi:10.3390/app11209508 · Cited by 67

A Review of Metal Surface Defect Detection Technologies in Industrial Applications

Qiao, Q., Hu, H., Ahmad, A. A., & Wang, K. (2025) · IEEE Access · doi:10.1109/access.2025.3544578 · Cited by 71

SDD-DETR: Surface Defect Detection for No-Service Aero-Engine Blades With Detection Transformer

Sun, X., Song, K., Wen, X., Wang, Y., & Yan, Y. (2024) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2024.3457829 · Cited by 59

Solder Joint Inspection on Printed Circuit Boards: A Survey and a Dataset

Ülger, F., Yüksel, S. E., Yılmaz, A., & Gökcen, D. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3277935 · Cited by 53

Advancements in AI-Driven detection and localisation of solar panel defects

Ghahremani, A., Adams, S. D., Norton, M., Khoo, S., & Kouzani, A. Z. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103104 · Cited by 51

Vision Transformer in Industrial Visual Inspection

Hütten, N., Meyes, R., & Meisen, T. (2022) · Applied Sciences · doi:10.3390/app122311981 · Cited by 55

Review of Surface-Defect Detection Methods for Industrial Products Based on Machine Vision

Wang, Q., Wang, M., Sun, J., Chen, D., & Shi, P. (2025) · IEEE Access · doi:10.1109/access.2025.3571297 · Cited by 53

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

A survey on machine and deep learning in semiconductor industry: methods, opportunities, and challenges

Huang, A. C., Meng, S. H., & Huang, T. J. (2023) · Cluster Computing · doi:10.1007/s10586-023-04115-6 · Cited by 53

Offshore wind turbine tower design and optimization: A review and AI-driven future directions

Ribeiro, J. A., Ribeiro, B. A., Pimenta, F., Tavares, S. M. O., Zhang, J., & Ahmed, F. (2025) · Applied Energy · doi:10.1016/j.apenergy.2025.126294 · Cited by 44

Zero-shot surface defect recognition with class knowledge graph

Li, Z., Gao, L., Gao, Y., Li, X., & Li, H. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101813 · Cited by 44

Defect Detection in Printed Circuit Boards Using Semi-Supervised Learning

Pham, T. T. A., Thoi, D. K. T., Choi, H., & Park, S. (2023) · Sensors · doi:10.3390/s23063246 · Cited by 43

An overview of traditional and advanced methods to detect part defects in additive manufacturing processes

Bhandarkar, V. V., Shahare, H. Y., Mall, A. P., & Tandon, P. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02483-3 · Cited by 48

Research on YOLOv7-based defect detection method for automotive running lights

Chen, J., Bai, S., Wan, G., & Li, Y. (2023) · Systems Science & Control Engineering · doi:10.1080/21642583.2023.2185916 · Cited by 40

Formation mechanism and detection and evaluation methods as well as repair technology of crack damage in fiber-reinforced composite wind turbine blade: a review

Cao, Z., Li, S., Li, C., Li, P., & Ko, T. J. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09230-z · Cited by 38

Transfer learning-based approach using new convolutional neural network classifier for steel surface defects classification

Ibrahim, A. A. M. S., & Tapamo, J. (2024) · Scientific African · doi:10.1016/j.sciaf.2024.e02066 · Cited by 39

Deep learning-based automated steel surface defect segmentation: a comparative experimental study

Sime, D. M., Wang, G., Zeng, Z. P., & Peng, B. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-15307-y · Cited by 33

An Improved YOLOX Model for Detecting Strip Surface Defects

Yi, C., Xu, B., Chen, J., Chen, Q., & Zhang, L. (2022) · steel research international · doi:10.1002/srin.202200505 · Cited by 29

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

Surface Defects Detection of Cylindrical High-Precision Industrial Parts Based on Deep Learning Algorithms: A Review

Li, W., Solihin, M. I., Saruchi, S. A., Astuti, W., Hong, L. W., & Kit, A. C. (2024) · Operations Research Forum · doi:10.1007/s43069-024-00337-5 · Cited by 28

SDDiff: Semi-supervised surface defect detection with Diffusion Probabilistic Model

Wang, X., Li, W., Liu, C., & Ouyang, N. (2024) · Measurement · doi:10.1016/j.measurement.2024.115276 · Cited by 29

Steel surface defect detection method based on improved YOLOv9

Chen, C., Lee, H. L., & Chen, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-10647-1 · Cited by 26

Faster Metallic Surface Defect Detection Using Deep Learning with燙hannel燬huffling

Yasir, S. M., & Ahn, H. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.035698 · Cited by 26

Volumetric nondestructive metrology for 3D semiconductor packaging: A review

Su, Y., Shi, J., Hsu, Y.-M., Ji, D.-Y., Suer, A., & Lee, J. (2023) · Measurement · doi:10.1016/j.measurement.2023.114065 · Cited by 25

A novel CNN ensemble framework for bearing surface defects classification based on transfer learning

Ma, J., Liu, M., Hu, S., Fu, J., Gui, C., & Yang, A. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac9c22 · Cited by 25

Lightweight defect detection network based on steel strip raw images

Huang, Y., Chen, Z., Chen, Z., Zhou, D., & Pan, E. (2025) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2025.110179 · Cited by 24

DSTEELNet: A Real-Time Parallel Dilated CNN with Atrous Spatial Pyramid Pooling for Detecting and Classifying Defects in Surface Steel Strips

Ragab, K. (2023) · Sensors · doi:10.3390/s23010544 · Cited by 24

Deep-Learning-Based Lithium Battery Defect Detection via Cross-Domain Generalization

Chen, X., Liu, M., Niu, Y., Wang, X., & Wu, Y. C. (2024) · IEEE Access · doi:10.1109/access.2024.3408718 · Cited by 23

The implication and evaluation of geometrical imperfections on manufactured surfaces

Mullany, B. A., Savio, E., Haitjema, H., & Leach, R. (2022) · CIRP Annals · doi:10.1016/j.cirp.2022.05.004 · Cited by 22

Classification of Silicon (Si) Wafer Material Defects in Semiconductor Choosers using a Deep Learning ShuffleNet-v2-CNN Model

Doss, R., Ramakrishnan, J., Kavitha, S., Ramkumar, S., Latha, G. C. P., & Ramaswamy, K. (2022) · Advances in Materials Science and Engineering · doi:10.1155/2022/1829792 · Cited by 22

Fabric Defect Segmentation System Based on a Lightweight GAN for Industrial Internet of Things

Li, B., Zou, Y., Zhu, R., Yao, W., Wang, J., & Wan, S. (2022) · Wireless Communications and Mobile Computing · doi:10.1155/2022/9680519 · Cited by 22

GelBelt: A Vision-Based Tactile Sensor for Continuous Sensing of Large Surfaces

Mirzaee, M. A., Huang, H.-J., & Yuan, W. (2025) · IEEE Robotics and Automation Letters · doi:10.1109/lra.2025.3527306 · Cited by 21

Local–global lightweight ViT model for mini/micro-LED-chip defect recognition

Wei, L., Cai, J., Wen, K., & Zhang, C. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106247 · Cited by 21

An Attention-Augmented Convolutional Neural Network With Focal Loss for Mixed-Type Wafer Defect Classification

Batool, U., Shapiai, M. I., Mostafa, S. A., & Ibrahim, M. Z. (2023) · IEEE Access · doi:10.1109/access.2023.3321025 · Cited by 21

A Systematic Review on Advancement of Image Segmentation Techniques for Fault Detection Opportunities and Challenges

Rahman, M. M., Rahman, S., Bhatt, S., & Faezipour, M. (2025) · Electronics · doi:10.3390/electronics14050974 · Cited by 20

Machine learning-driven advances in nanotechnology: From materials design to process optimization – A review

Mim, J. J., Mamun, A. A., Nayem, M. H., Mahmud, S., Nath, A., Rahman, S., Fidal, S. A., & Hossain, N. (2025) · Materials Today Communications · doi:10.1016/j.mtcomm.2025.114485 · Cited by 20

Steel Strip Quality Assurance With YOLOV7-CSF: A Coordinate Attention and SIoU Fusion Approach

Raj, G. D., & Prabadevi, B. (2023) · IEEE Access · doi:10.1109/access.2023.3333894 · Cited by 20

Category-level selective dual-adversarial network using significance-augmented unsupervised domain adaptation for surface defect detection

Zhang, S., Su, L., Gu, J., Li, K., Wu, W., & PECHT, M. G. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121879 · Cited by 19

Few-shot defect recognition for the multi-domain industry via attention embedding and fine-grained feature enhancement

Su, Y., Yan, P., Lin, J., Wen, C., & Fan, Y. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.111265 · Cited by 19

A pushing-grasping collaborative method based on deep Q-network algorithm in dual viewpoints

Peng, G., Liao, J., Guan, S., Yang, J., & Li, X. (2022) · Scientific Reports · doi:10.1038/s41598-022-07900-2 · Cited by 18

Moving towards agriculture 4.0: An AI-AOI carrot inspection system with accurate geometric properties

Liong, S., Wu, Y.-L., Liong, G.-B., & Gan, Y. S. (2023) · Journal of Food Engineering · doi:10.1016/j.jfoodeng.2023.111632 · Cited by 18

An Overview of CNN-Based Image Analysis in Solar Cells, Photovoltaic Modules, and Power Plants

Matusz-Kalász, D., Bodnár, I., & Jobbágy, M. (2025) · Applied Sciences · doi:10.3390/app15105511 · Cited by 18

Steel Surface Defect Detection Using Learnable Memory Vision Transformer

Ayon, S. T. K., Siraj, F. M., & Uddin, J. (2025) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2025.058361 · Cited by 18

SWRD: A Dataset of Radiographic Image of Seam Weld for Defect Detection

Zhao, X., Wu, J., Zhang, B., Wen, H., Wang, X., Li, Y., & Yu, X. (2025) · Journal of Nondestructive Evaluation · doi:10.1007/s10921-025-01186-w · Cited by 16

A deep residual neural network for semiconductor defect classification in imbalanced scanning electron microscope datasets

de la Rosa, F. L., Gómez-Sirvent, J. L., Morales, R., Sánchez-Reolid, R., & Fernández‐Caballero, A. (2022) · Applied Soft Computing · doi:10.1016/j.asoc.2022.109743 · Cited by 16

Deep Learning-Based Object Detection Strategies for Disease Detection and Localization in Chest X-Ray Images

Cheng, Y.-C., Hung, Y.-C., Huang, G., Chen, T., Lu, N., Liu, K., & Lin, K.-H. (2024) · Diagnostics · doi:10.3390/diagnostics14232636 · Cited by 16

MSANet: efficient detection of tire defects in radiographic images

Zhao, M., Zheng, Z., Sun, Y., Chang, Y., Tian, C., & Zhang, Y. (2022) · Measurement Science and Technology · doi:10.1088/1361-6501/ac85d1 · Cited by 16

A vision-based hole quality assessment technique for robotic drilling of composite materials using a hybrid classification model

Lee, S., Simeth, A., Hinchy, E. P., Plapper, P., O’Dowd, N. P., & McCarthy, C. T. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-12290-4 · Cited by 15

Multi-dimensional feature extraction-based deep encoder–decoder network for automatic surface defect detection

Üzen, H., Türkoğlu, M., & Hanbay, D. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07885-z · Cited by 15

Detecting dyeing machine entanglement anomalies by using time series image analysis and deep learning techniques for dyeing-finishing process

Wang, C.-C., & Kuo, C.-H. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101852 · Cited by 15

Unsupervised fabric defect detection based on multiscale image reconstruction and structural similarity assessment

Yu, Z., Xu, Y., Wang, Y., Wang, Y., & Sheng, X. (2023) · Coloration Technology · doi:10.1111/cote.12744 · Cited by 13

Real-time tunnel lining leakage image semantic segmentation via multiple attention mechanisms

Tan, Y., Li, X., Lai, J., & Ai, J. (2024) · Measurement Science and Technology · doi:10.1088/1361-6501/ad1ddb · Cited by 13

Attentive context and semantic enhancement mechanism for printed circuit board defect detection with two-stage and multi-stage object detectors

Kiobya, T., Zhou, J., Maiseli, B. J., & Khan, M. (2024) · Scientific Reports · doi:10.1038/s41598-024-69207-8 · Cited by 13

Advancing Industrial Object Detection Through Domain Adaptation: A Solution for Industry 5.0

Fatima, Z., Zardari, S., & Tanveer, M. H. (2024) · Actuators · doi:10.3390/act13120513 · Cited by 13

Next-generation Vision Inspection Systems: a pipeline from 3D model to ReCo file

Lupi, F., Freitas, N., Arvana, M., Rocha, A. D., Maffei, A., Barata, J., & Lanzetta, M. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02456-6 · Cited by 12

Integrated Circuit Packaging Defect Analysis and Deep Learning Detection Method

Liu, F., Wang, H., Feng, P., & Zeng, L. (2024) · IEEE Transactions on Components Packaging and Manufacturing Technology · doi:10.1109/tcpmt.2024.3447040 · Cited by 12

OHEH-RTDETR: an improved RT-DETR detection model based on frequency layered processing and advanced feature selection

Wang, H., Zhang, Y., & Wu, W. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/adc1f3 · Cited by 12

Recognition of unknown wafer defect via optimal bin embedding technique

Chu, M. S., Park, S., Jeong, J., Joo, K.-H., Lee, Y., & Kang, J. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-09447-y · Cited by 12

Defect-Net: a new CNN model for steel surface defect classification

Abbes, W., Elleuch, J. F., & Sellami, D. (2024) · International Symposium on Signal, Image, Video and Communications (ISIVC) · doi:10.1109/isivc61350.2024.10577945 · Cited by 12

DDEG: Addressing data scarcity in additive manufacturing defect detection through joint generation of CT images and defect annotations

Han, R., Wang, Y., Guo, W., Wang, C., Zhang, Y., Zi, Y., & Zhao, J. (2025) · Advanced Engineering Informatics · doi:10.1016/j.aei.2025.103619 · Cited by 11

Automated visual inspection of manufactured parts using deep convolutional neural networks and transfer learning

Weiher, K., Rieck, S., Pankrath, H., Beuß, F., Geist, M., Sender, J., & Fluegge, W. (2023) · Procedia CIRP · doi:10.1016/j.procir.2023.09.088 · Cited by 11

Input-guidance diffusion model for unknown defect patterns detection in wafer bin map

Moon, S., & Kim, S. B. (2024) · Advanced Engineering Informatics · doi:10.1016/j.aei.2024.103078 · Cited by 11

Surrogate-based optimization framework for enhancing SMT process quality and productivity in electronics manufacturing services

Lee, C., Lin, K.-X., & Chou, C.-W. (2025) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-025-15032-w · Cited by 11

Quantum Deep Learning for Steel Industry Computer Vision Quality Control.

Villalba-Díez, J., Ordieres-Meré, J. B., González‐Marcos, A., & Larzabal, A. S. (2022) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2022.04.216 · Cited by 11

Exploiting CNN’s visual explanations to drive anomaly detection

Fraccaroli, M., Bizzarri, A., Casellati, P., & Lamma, E. (2023) · Applied Intelligence · doi:10.1007/s10489-023-05177-0 · Cited by 10

Machine Learning Techniques for Quality Control in Textile Fabric Manufacturing

Hariharan, V. S., & Sivaraman, K. (2024) · International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss60660.2024.10625436 · Cited by 10

An ESG-ConvNeXt network for steel surface defect classification based on hybrid attention mechanism

Zhang, N., Liu, Z., Zhang, E., Chen, Y., & Yue, J. (2025) · Scientific Reports · doi:10.1038/s41598-025-88958-6 · Cited by 10

Automated Defect Identification System in Printed Circuit Boards Using Region-Based Convolutional Neural Networks

Weerakkody, K. D., Balasundaram, R. J., Osagie, E. R., & Al-Ani, J. A. (2025) · Electronics · doi:10.3390/electronics14081542 · Cited by 10

ACPP-Net: Enhancing Strip Steel Surface Defect Detection With Efficient Adaptive Convolution and Channel-Spatial Pyramid Pooling

Li, R., Hou, K., Zhu, M., Dai, Q., Ni, J., Liu, X., & Li, X. (2024) · IEEE Access · doi:10.1109/access.2024.3481031 · Cited by 9

A novel context-adaptive multi-scale defect detection method for object surface defects

Zhang, H., Liu, J., Pan, D., Jiang, Z., & Dong, J. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.130927 · Cited by 9

On the precision models of fringe projection profilometry: unification, simplification and connection

Lv, S., Huang, N., Zou, Y., Liu, C., Long, S. Y., Tang, D., Jiang, X., & Kemao, Q. (2026) · Light Science & Applications · doi:10.1038/s41377-026-02300-x · Cited by 9

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

An Artificial-Intelligence-Driven Predictive Model for Surface Defect Detections in Medical MEMS

Amini, A., Kanfoud, J., & Gan, T. (2021) · Sensors · doi:10.3390/s21186141 · Cited by 9

Defect Detection of Scroll Fixed Using AI Machine Vision Inspection

Lee, J.-S., Yun, K.-C., & Park, J. K. (2024) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-024-01125-1 · Cited by 8

A deep learning approach to perform defect classification of freeze-dried product

Herve, Q., Ipek, N., Verwaeren, J., & Beer, T. D. (2025) · International Journal of Pharmaceutics · doi:10.1016/j.ijpharm.2024.125127 · Cited by 9

3D point cloud analysis for surface quality inspection: A steel parts use case

Ntoulmperis, M., Catti, P., Discepolo, S., van de Kamp, W., Castellini, P., Nikolakis, N., & Alexopoulos, K. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.01.074 · Cited by 8

Systematic Review of Steel Surface Defect Detection Methods on the Open Access Datasets of Severstal and the Northeastern University (NEU)

Aşar, E., & Özgür, A. (2024) · Engineering materials · doi:10.1007/978-3-031-57468-9_3 · Cited by 8

Wafer Surface Semiconductor Defect Classification Using Convolution Neural Network Based Improved Faster R-CNN

Babu, P., Habelalmateen, M. I., Srikanteswara, R., Reddy, R. A., & Purushotham, N. (2024) · International Conference on Data Science and Information System (ICDSIS) · doi:10.1109/icdsis61070.2024.10594305 · Cited by 8

Advances in welding sensing information processing and modelling technology: an overview

Mehta, A., & Vasudev, H. (2024) · Journal of Adhesion Science and Technology · doi:10.1080/01694243.2024.2388141 · Cited by 8

Relationship between the kernel size of a convolutional layer and the optical point spread function in ghost imaging using deep learning for identifying defect locations

Kataoka, S., Mizutani, Y., Uenohara, T., Takaya, Y., & Matoba, O. (2022) · Applied Optics · doi:10.1364/ao.460145 · Cited by 8

Domain Transfer for Surface Defect Detection using Few-Shot Learning on Scarce Data

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