Steel defect classification with Max-Pooling Convolutional Neural Networks

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

Histograms of Oriented Gradients for Human Detection

Dalal, N., & Triggs, B. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.177 · Cited by 33,584

Learning Internal Representations by Error Propagation

Rumelhart, D. E., Hinton, G. E., & Williams, R. J. (1985) · doi:10.21236/ada164453 · Cited by 16,501

Bagging predictors

Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325

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

A comparative study of texture measures with classification based on featured distributions

Ojala, T., Pietikäinen, M., & Harwood, D. (1996) · Pattern Recognition · doi:10.1016/0031-3203(95)00067-4 · Cited by 6,882

Receptive fields and functional architecture of monkey striate cortex

Hubel, D. H., & Wiesel, T. N. (1968) · The Journal of Physiology · doi:10.1113/jphysiol.1968.sp008455 · Cited by 6,685

Neocognitron: A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position

Fukushima, K. (1980) · Biological Cybernetics · doi:10.1007/bf00344251 · Cited by 5,759

Pattern Recognition and Machine Learning

Neal, R. M. (2007) · Technometrics · doi:10.1198/tech.2007.s518 · Cited by 4,629

Best practices for convolutional neural networks applied to visual document analysis

Simard, P. Y., Steinkraus, D., & Platt, J. C. (2005) · IAPR International Conference on Document Analysis and Recognition (ICDAR) · doi:10.1109/icdar.2003.1227801 · Cited by 2,862

Welding Surface Inspection of Armatures via CNN and Image Comparison

Feng, T., Huang, S., Liu, J., Wang, J., & Fang, X. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3079334 · Cited by 8

Design of Multi-Receptive Field Fusion-Based Network for Surface Defect Inspection on Hot-Rolled Steel Strip Using Lightweight Dataset

Tang, W.-P., Liong, S., Chen, C., Tsai, M., Hsieh, P.-C., Tsai, Y., Chen, S.-H., & Wang, K.-C. (2021) · Applied Sciences · doi:10.3390/app11209473 · Cited by 9

Deep representation and stereo vision based vehicle detection

Cai, Y., Chen, X., Wang, H., & Chen, L. (2015) · IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems · doi:10.1109/cyber.2015.7287953 · Cited by 8

Defect Detection of Metal Nuts Applying Convolutional Neural Networks

Sauter, D., Schmitz, A., Dikici, F., Baumgartl, H., & Buettner, R. (2021) · IEEE Annual International Computer Software and Applications Conference (COMPSAC) · doi:10.1109/compsac51774.2021.00043 · Cited by 7

Security in defect detection: A new one-pixel attack for fooling DNNs

Wang, P., Li, Q., Li, D., Meng, S., Bilal, M., & Mukherjee, A. (2023) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2023.101689 · Cited by 7

About some approaches to problem of metals and alloys microstructures classification based on neural network technologies

Klestov, R., Klyuev, A. V., & Stolbov, V. Y. (2018) · International Conference "Actual Issues of Mechanical Engineering" (AIME) · doi:10.2991/aime-18.2018.56 · Cited by 7

Deep Learning Framework for Steel Surface Defects Classification

Singari, R. M., Singla, K., & Chawla, G. (2019) · International Journal of Advanced Production and Industrial Engineering · doi:10.35121/ijapie201901135 · Cited by 7

Online metallic surface defect detection using deep learning

ÇEREZCİ, F., Kazan, S., Öz, M. A. N., Öz, C., Taşcı, T., Hızal, S., & Altay, Ç. (2020) · Emerging Materials Research · doi:10.1680/jemmr.20.00197 · Cited by 7

Yarn-dyed fabric defect classification based on convolutional neural network

Jing, J., Dong, A., & Li, P. (2017) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.2281978 · Cited by 7

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Aqeel, M., Sharifi, S., Cristani, M., & Setti, F. (2024) · Communications in computer and information science · doi:10.1007/978-3-031-66705-3_11 · Cited by 6

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Tang, Y., Wang, H., Zhou, Q., & Sun, B. (2024) · IEEE Access · doi:10.1109/access.2024.3374131 · Cited by 7

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Jeong, S., Song, J., & Lee, S. J. (2025) · International Journal of Control Automation and Systems · doi:10.1007/s12555-024-0550-8 · Cited by 6

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Alkapov, R., Konyshev, A. V., Vetoshkin, N., Valkevich, N., & Kostenetskiy, P. S. (2018) · Global Smart Industry Conference (GloSIC) · doi:10.1109/glosic.2018.8570081 · Cited by 6

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Bi, Z., Bi, Q. W. Z., Wu, M. S. Q., & Shan, W. Z. M. (2022) · 網際網路技術學刊 · doi:10.53106/160792642022112306022 · Cited by 6

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Lin, Y.-H., & Ruan, S.-J. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3252632 · Cited by 6

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Su, J., Zhang, H., Przystupa, К., & Кочан, О. В. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.3.033019 · Cited by 7

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Gao, F., Zhu, Q., Shao, G., Su, Y., Yang, J., & Yu, X. (2022) · CAAI Transactions on Intelligence Technology · doi:10.1049/cit2.12407 · Cited by 8

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Mittal, S., Chopra, C., Trivedi, A., & Chanak, P. (2019) · International Conference on Issues and Challenges in Intelligent Computing Techniques · doi:10.1109/icict46931.2019.8977696 · Cited by 6

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Liu, G., Yang, N., & Guo, L. (2020) · Applied Sciences · doi:10.3390/app10186215 · Cited by 6

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Liu, W., Zhang, J., Su, Z., Zhou, Z., & Liu, L. (2021) · Sensors · doi:10.3390/s21206868 · Cited by 6

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Tripicchio, P., & D’Avella, S. (2022) · IntechOpen eBooks · doi:10.5772/intechopen.101951 · Cited by 6

Convolutional Neural Networks for night-time animal orientation estimation

Wagner, R., Thom, M., Gabb, M., Limmer, M., Schweiger, R., & Rothermel, A. (2013) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2013.6629488 · Cited by 6

Deflectometric data segmentation for surface inspection: a fully convolutional neural network approach

Maestro-Watson, D., Balzategui, J., Eciolaza, L., & Arana-Arexolaleiba, N. (2020) · Journal of Electronic Imaging · doi:10.1117/1.jei.29.4.041007 · Cited by 6

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Bai, W., Bu, C., Chen, P., Huang, X. H., Liu, G., Li, R., & Tang, Q. (2025) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2025.2508427 · Cited by 5

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Wang, Y.-B., Sun, Z., Ma, X.-Y., Lin, B., Stroetmann, R., & Li, G.-Q. (2025) · Journal of Constructional Steel Research · doi:10.1016/j.jcsr.2025.110016 · Cited by 5

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Üzen, H., Türkoğlu, M., Öztürk, D., & Hanbay, D. (2024) · Signal Image and Video Processing · doi:10.1007/s11760-024-03355-2 · Cited by 6

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Bilal, M., Podishetti, R., Koval, L., Gaafar, M., Großmann, D., & Bregulla, M. (2024) · IEEE Access · doi:10.1109/access.2024.3454692 · Cited by 5

Recommendation System for Material Scientists Based on Deep Learn Neural Network

Klyuev, A. V., Klestov, R., Bartolomey, M., & Рогожников, А. Г. (2018) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-02351-5_26 · Cited by 5

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Song, Z., Yuan, Z., & Liu, T. (2019) · Machine Vision Applications · doi:10.23919/mva.2019.8757969 · Cited by 5

Computerized screening of G-protein coupled receptors to identify and characterize olfactory receptors

Zhang, R., Wang, P., Yu, S., Hansbro, P. M., & Wang, H. (2020) · Journal of Toxicology and Environmental Health · doi:10.1080/15287394.2019.1709305 · Cited by 5

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Vu, T. T. H., Vo, T. H., Tran, L. H., Choi, J., Vo, T. T., Ly, C. D., Nguyen, T. P., Mondal, S., & Oh, J. (2024) · Engineering Reports · doi:10.1002/eng2.12933 · Cited by 4

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Gong, B., & Zhu, Z. (2024) · Metals · doi:10.3390/met14070761 · Cited by 4

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Garcia, D. F., García, I., delaCalle, F. J., & Usamentiaga, R. (2018) · International Conference on Mathematics and Computers in Sciences and Industry (MCSI) · doi:10.1109/mcsi.2018.00019 · Cited by 4

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Yunani, J. L., & Medvedev, M. V. (2019) · International Multi-Conference on Industrial Engineering and Modern Technologies (FarEastCon) · doi:10.1109/fareastcon.2019.8933868 · Cited by 4

Fabric Defect Recognition Method Based on Deep Convolution Neural Network

张, 卷. (2021) · Computer Science and Application · doi:10.12677/csa.2021.112035 · Cited by 4

Research on Defect Recognition of Lithium Battery Pole Piece Based on Deep Learning

Li, J., Li, L., & Xu, C. (2021) · E3S Web of Conferences · doi:10.1051/e3sconf/202126101021 · Cited by 4

Application of the Convolutional Neural Network for Classification of the Aluminum Alloys Based on Their Microstructural Characteristics

Moon, G., Seo, H.-I., Seo, D.-H., & Lee, E. (2023) · JOM · doi:10.1007/s11837-023-06075-z · Cited by 4

FPGA-Based Optical Surface Inspection of Wind Turbine Rotor Blades Using Quantized Neural Networks

Giefer, L. A., Staar, B., & Freitag, M. (2020) · Electronics · doi:10.3390/electronics9111824 · Cited by 4

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Zhang, Z., Lv, C., Sun, M., & Wang, Z. (2020) · International Conference on Dependable Systems and Their Applications (DSA) · doi:10.1109/dsa51864.2020.00071 · Cited by 4

Night time road curvature estimation based on Convolutional Neural Networks

Hartmann, O., Schweiger, R., Wagner, R., Schüle, F., Gabb, M., & Dietmayer, K. (2013) · IEEE Intelligent Vehicles Symposium · doi:10.1109/ivs.2013.6629571 · Cited by 4

Automatic monitoring of steel strip positioning error based on semantic segmentation

Lemos, A. D. F., da Silva, L. A. R., & Nagy, B. V. (2020) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-020-05859-w · Cited by 4

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Wu, M., Sharapov, J., Anderson, M. W., Lu, Y. X., & Wu, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-00238-5 · Cited by 3

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Das, B., Hulipalled, P., Algur, V., Jain, M., Karmakar, A., & Barat, K. (2025) · Journal of Materials Engineering and Performance · doi:10.1007/s11665-025-12401-0 · Cited by 3

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Jayawardena, A., Sul, J.-H., Moratuwage, D., Wijayaraja, J. L., & Piyathilaka, L. (2026) · Electronics · doi:10.3390/electronics15122727 · Cited by 9

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Akula, K. K., GEGOV, A. E., & Arabikhan, F. (2023) · WSEAS Transactions on Computers archive · doi:10.37394/23205.2023.22.24 · Cited by 3

Coupling of unsupervised and supervised deep learning-based approaches for surface anomaly detection

Rački, D., Tomaževič, D., & Skočaj, D. (2024) · Journal of Electronic Imaging · doi:10.1117/1.jei.33.3.031207 · Cited by 3

Quantifying the effect of X-ray scattering for data generation in real-time defect detection

Andriiashen, V., van Liere, R., van Leeuwen, T., & Batenburg, K. J. (2024) · Journal of X-Ray Science and Technology · doi:10.3233/xst-230389 · Cited by 3

Abnormal Detection of Commutator Surface Defects Based on YOLOv8

Li, Z., Kwan, B.-H., Tham, M., Ng, O.-E., & Wang, P. S. P. (2024) · International Journal of Pattern Recognition and Artificial Intelligence · doi:10.1142/s0218001424500137 · Cited by 3

Optimized Adaptive Multi-Scale Architecture for Surface Defect Recognition

Chang, X., Wang, Y., Zhang, H., Adamyk, B., & Yan, L. (2025) · Algorithms · doi:10.3390/a18080529 · Cited by 3

Detection of Cardiomegaly from Chest X-rays using Otsu Algorithm and Convolutional Neural Network

Mohare, S., Dawani, H., Rathi, V., & Oswal, S. (2019) · IEEE International Conference for Convergence in Technology (I2CT) · doi:10.1109/i2ct45611.2019.9033786 · Cited by 3

Data-Driven Predictive Maintenance: A Methodology Primer

Cerquitelli, T., Nikolakis, N., Morra, L., Bellagarda, A., Orlando, M., Salokangas, R., Saarela, O., Hietala, J., Kaarmila, P., & Macii, E. (2021) · Information fusion and data science · doi:10.1007/978-981-16-2940-2_3 · Cited by 3

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Shi, G., Che, J. W., Hu, X., Li, Y. L., Wang, L., & Mao, Y. H. (2022) · Surface Topography Metrology and Properties · doi:10.1088/2051-672x/ac929b · Cited by 3

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Mordia, R., & Verma, A. K. (2024) · Transportation Infrastructure Geotechnology · doi:10.1007/s40515-024-00418-2 · Cited by 6

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Nagorny, P., Lacombe, T., Muller, T., Favrelière, H., Pairel, É., & Pillet, M. (2019) · International Conference on Quality Control by Artificial Vision (QCAV) · doi:10.1117/12.2522062 · Cited by 3

Batch covariance neural network for image recognition

Zheng, T., Wang, Q., Shen, Y., Ma, X., & Lin, X. (2022) · Image and Vision Computing · doi:10.1016/j.imavis.2022.104446 · Cited by 2

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Panda, A., Naskar, R., & Pal, S. (2019) · International symposium on image and signal processing and analysis/ISPA ... · doi:10.1109/ispa.2019.8868495 · Cited by 3

Applications of Deep Learning Techniques to Wood Anomaly Detection

Celik, Y., Güney, S., & Dengiz, B. (2022) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-031-10388-9_27 · Cited by 3

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Raut, R., Jadhav, P., & Bodas, A. (2023) · Lecture notes in networks and systems · doi:10.1007/978-981-19-6088-8_63 · Cited by 3

Neural Network Model for Assessing the Physical and Mechanical Properties of a Metal Material Based on Deep Learning

Klyuev, A. V., Klestov, R., & Stolbov, V. Y. (2020) · Journal of Digital Science · doi:10.33847/2686-8296.2.1_2 · Cited by 3

Optical Metallography of Fusion-Based Additively Manufactured Metals

Seita, M., Wittwer, M., & Wang, X. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-819726-4.00030-2 · Cited by 3

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Lu, X., & Fan, W. (2022) · International Conference on Innovation in Artificial Intelligence (ICIAI) · doi:10.1145/3529466.3529471 · Cited by 2

Alternative Data Augmentation for Industrial Monitoring Using Adversarial Learning

Mertes, S., Margraf, A., Geinitz, S., & André, E. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-37320-6_1 · Cited by 2

Combining unsupervised and supervised deep learning approaches for surface anomaly detection

Rački, D., Tomaževič, D., & Skočaj, D. (2023) · International Conference on Quality Control by Artificial Vision (QCAV) · doi:10.1117/12.2688559 · Cited by 3

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Engelhardt, A., Decke, J., Meier, D., Dulig, F., Ragunathan, R., Wegener, T., Sick, B., & Niendorf, T. (2023) · Advanced Engineering Materials · doi:10.1002/adem.202300876 · Cited by 2

AHŞAP HAM MADDELERDE YÜZEY HATASINI BELİRLEMEK İÇİN GÖRÜNTÜ İŞLEME TABANLI KALİTE KONTROL SİSTEMİ

Celik, Y., Dengiz, B., & Güney, S. (2023) · Mühendislik Bilimleri ve Tasarım Dergisi · doi:10.21923/jesd.1248010 · Cited by 3

Semantic Web-Based Image Super-Resolution for Enhanced Steel Surface Defect Detection

Hao, C., & Gu, H. (2024) · International Journal on Semantic Web and Information Systems · doi:10.4018/ijswis.358452 · Cited by 2

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Sekaran, S. S., & Subaji, M. (2024) · Journal of Advances in Information Technology · doi:10.12720/jait.15.12.1355-1365 · Cited by 2

Solitary-wave-based deep learning for compressive strength estimation in cementitious materials

Yoon, S.-Y., An, B. H., Yeun, C. Y., Damiani, E., Khalfan, M. M. A., & Kim, T. (2025) · International Journal of Mechanical Sciences · doi:10.1016/j.ijmecsci.2025.110170 · Cited by 2

Steel surface defect detection and localization based on SVD and two-side compressive measurements

Gao, J., Wen, C., & Liu, M. (2014) · Chinese Control and Decision Conference (CCDC) · doi:10.1109/ccdc.2014.6852386 · Cited by 2

Deep learning for zero-defect inkjet-printing of electronics

Minnette, F., & Zambal, S. (2021) · IEEE International Workshop on Metrology for Industry 4.0 & IoT (MetroInd4.0&IoT) · doi:10.1109/metroind4.0iot51437.2021.9488493 · Cited by 2

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Zhou, Z., Lan, C., & Gao, Z. (2022) · Asia Conference on Algorithms, Computing and Machine Learning (CACML) · doi:10.1109/cacml55074.2022.00053 · Cited by 2

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Zhang, J., Guo, D., Wu, Y., Xu, X., & Liu, H. (2023) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2023.3258743 · Cited by 2

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Zhang, S., Pei, Z., & Ren, Z. (2024) · International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID) · doi:10.1117/12.3026364 · Cited by 2

Terminal strip detection and recognition based on improved YOLOv7-tiny and MAH-CRNN+CTC models

Zhijun, G., Weiming, L., Qiujie, C., & Zou, H. (2024) · Frontiers in Energy Research · doi:10.3389/fenrg.2024.1345574 · Cited by 2

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Agour, M., Falldorf, C., Staar, B., von Freyberg, A., Fischer, A. M., Lütjen, M., & Bergmann, R. B. (2018) · MATEC Web of Conferences · doi:10.1051/matecconf/201819015008 · Cited by 2

AnoNet: Weakly Supervised Anomaly Detection in Textured SurfacesPreprint

Minhas, M. S., & Zelek, J. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1911.10608 · Cited by 2

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Федосов, А. В., & Чумаченко, Г. В. (2020) · Steel in Translation · doi:10.3103/s0967091220110066 · Cited by 1

IchthyNet: An Ensemble Method for the Classification of In Situ Marine Zooplankton Shadowgraph Images

Slocum, B., & Penta, B. (2025) · Oceans · doi:10.3390/oceans6010007 · Cited by 2

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Wang, Q., Xie, Z., Zeng, Y., & Zhang, H. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-9881-3_12 · Cited by 1

DDoS attack detection using deep transfer learning and FFT-based data transformation

Shen, G., Zhang, Y., Zhong, G., & Gong, Z. (2026) · The Journal of Supercomputing · doi:10.1007/s11227-026-08253-7 · Cited by 1

Proposal of local feature vector focusing on the differences among neighboring ROI’s

Muto, K., Matsubara, T., & Koshimizu, H. (2018) · International Workshop on Advanced Imaging Technology (IWAIT) · doi:10.1109/iwait.2018.8369637 · Cited by 1

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Andrade, D., Simiand, M., & Barreriro, A. J. (2020) · Iron & Steel Technology Conference and Exposition (AISTech) · doi:10.33313/380/214 · Cited by 1

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Wang, D., Liu, Y., Du, W., & Zhu, R. (2022) · Journal of Computer-Aided Design & Computer Graphics · doi:10.3724/sp.j.1089.2022.19056 · Cited by 1

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Zenati, T., Figliuzzi, B., & Ham, S. H. (2023) · Chemometrics and Intelligent Laboratory Systems · doi:10.1016/j.chemolab.2023.104879 · Cited by 1

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Jiang, H., Li, Y., Li, C., & Li, Y. (2023) · International Symposium on Computer Engineering and Intelligent Communications (ISCEIC) · doi:10.1109/isceic59030.2023.10271217 · Cited by 1

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Pan, W., Long, Z., Li, X., Li, G., Huang, T., Liang, Y., & Zhai, Y. (2024) · IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA) · doi:10.1109/civemsa58715.2024.10586533 · Cited by 1

Research on bridge surface defects based on YOLOv7

jiang, Q., zhang, H., zhang, H., & jiang, W. (2024) · International Conference on Electronic Information Engineering, Big Data and Computer Technology (EIBDCT) · doi:10.1117/12.3031149 · Cited by 1

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Hossen, M. S., Sahoo, A., & Wang, H. (2024) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn60899.2024.10651274 · Cited by 1

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Tianto, E., Samudera, A. C., Adeta, F., & Nadia, N. (2024) · International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE) · doi:10.1109/icitisee63424.2024.10730161 · Cited by 1

MT-RAM: Multi Task-Recurrent Attention Model for partially observable image anomaly classification and localization

Guo, J., Han, C., Ma, Y., & Zhang, C. (2024) · IISE Transactions · doi:10.1080/24725854.2024.2425292 · Cited by 1

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Santos, A. A., Rocha, F. A. S., Azpúrua, H., Reis, A. J. R., & Guimarães, F. G. (2019) · Anais do 14º Simpósio Brasileiro de Automação Inteligente · doi:10.17648/sbai-2019-111300 · Cited by 1

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Lee, K. J., Kwon, J. W., Min, S., & Yoon, J. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i08.7020 · Cited by 1

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Wang, K., Deng, J., Xu, L., Tang, C., Pei, Z., & Wang, H. (2020) · Chinese Control Conference (CCC) · doi:10.23919/ccc50068.2020.9189086 · Cited by 1

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Moro, M., Andreatta, C., Corridori, C., Rota, P., & Sebe, N. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68799-1_37 · Cited by 1

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