Most cited papers in Industrial Vision Systems and Defect Detection
What are the most cited papers in Industrial Vision Systems and Defect Detection?
The most cited paper in Industrial Vision Systems and Defect Detection is "The Knowledge-creating company: How Japanese companies create the dynamics of innovation" (H. Takeuchi, 1997), with 13,221 citations. Next come "YOLOv4: Optimal Speed and Accuracy of Object Detection" (10,541) and "A Concordance Correlation Coefficient to Evaluate Reproducibility" (8,920).
Showing 50 of 100 papers. Every paper with more than 551 citations is listed.
| # | Paper | ||
|---|---|---|---|
| 1 | The Knowledge-creating company: How Japanese companies create the dynamics of innovation H. Takeuchi · Research Policy | Dec 1997 | 13,221 |
| 2 | YOLOv4: Optimal Speed and Accuracy of Object Detection Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao · arXiv (Cornell University) | 23 Apr 2020 | 10,541 |
| 3 | A Concordance Correlation Coefficient to Evaluate Reproducibility Lawrence I-Kuei Lin · Biometrics | Mar 1989 | 8,920 |
| 4 | R-FCN: Object Detection via Region-based Fully Convolutional Networks Jifeng Dai, Yi Li, Kaiming He et al. · arXiv (Cornell University) | 20 May 2016 | 3,434 |
| 5 | Multi-view Convolutional Neural Networks for 3D Shape Recognition Hang Su, Subhransu Maji, Evangelos Kalogerakis et al. | Dec 2015 | 3,408 |
| 6 | Object Detection in 20 Years: A Survey Zhengxia Zou, Keyan Chen, Zhenwei Shi et al. · Proceedings of the IEEE | 27 Jan 2023 | 3,119 |
| 7 | The psychology of computer vision Azriel Rosenfeld · Artificial Intelligence | 1976 | 2,999 |
| 8 | Intelligent Manufacturing in the Context of Industry 4.0: A Review Ray Y. Zhong, Xun William Xu, Eberhard Klotz et al. · Engineering | Oct 2017 | 2,894 |
| 9 | Digital twin-driven product design, manufacturing and service with big data Fei Tao, Jiangfeng Cheng, Qinglin Qi et al. · The International Journal of Advanced Manufacturing Technology | 16 Mar 2017 | 2,880 |
| 10 | Deep learning and its applications to machine health monitoring Rui Zhao, Ruqiang Yan, Zhenghua Chen et al. · Mechanical Systems and Signal Processing | 14 Jun 2018 | 2,854 |
| 11 | 1D convolutional neural networks and applications: A survey Kiranyaz, Mustafa Serkan, Onur Avcı, Osama Abdeljaber et al. · Qatar University QSpace (Qatar University) | 2021 | 2,836 |
| 12 | Image Analysis Using Mathematical Morphology Robert M. Haralick, Stanley R. Sternberg, Xinhua Zhuang · IEEE Transactions on Pattern Analysis and Machine Intelligence | Jul 1987 | 2,733 |
| 13 | A survey of thresholding techniques Prasanna K. Sahoo, Soheil Soltani, Alexander K.C. Wong · Computer Vision Graphics and Image Processing | Feb 1988 | 2,670 |
| 14 | Least squares fitting of a straight line with correlated errors Derek York · Earth and Planetary Science Letters | 1968 | 2,354 |
| 15 | A New Convolutional Neural Network-Based Data-Driven Fault Diagnosis Method Long Wen, Xinyu Li, Liang Gao et al. · IEEE Transactions on Industrial Electronics | 17 Nov 2017 | 2,197 |
| 16 | Minimum error thresholding Josef Kittler, John Illingworth · Pattern Recognition | 1986 | 2,178 |
| 17 | The CIEDE2000 color-difference formula: Implementation notes, supplementary test data, and mathematical observations Gaurav Sharma, Wencheng Wu, Edul N. Dalal · Color Research & Application | 2004 | 2,172 |
| 18 | Literature review of Industry 4.0 and related technologies Ercan Öztemel, Samet GÜRSEV · Journal of Intelligent Manufacturing | 24 Jul 2018 | 2,132 |
| 19 | YOLOv8: A Novel Object Detection Algorithm with Enhanced Performance and Robustness Rejin Varghese, M. Sambath | 18 Apr 2024 | 1,846 |
| 20 | Classification in the Presence of Label Noise: A Survey Benoît Frénay, Michel Verleysen · IEEE Transactions on Neural Networks and Learning Systems | 31 Jan 2014 | 1,800 |
| 21 | Data-driven smart manufacturing Fei Tao, Qinglin Qi, Ang Liu et al. · Journal of Manufacturing Systems | 17 Jan 2018 | 1,781 |
| 22 | Deep learning for smart manufacturing: Methods and applications Jinjiang Wang, Yulin Ma, Laibin Zhang et al. · Journal of Manufacturing Systems | 8 Jan 2018 | 1,717 |
| 23 | Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison Qinglin Qi, Fei Tao · IEEE Access | 2018 | 1,695 |
| 24 | Image processing, analysis and machine vision Kari P. Saarinen · Signal Processing | 1994 | 1,507 |
| 25 | Working Set Selection Using Second Order Information for Training Support Vector Machines Rong-En Fan, Pai-Hsuen Chen, Chih‐Jen Lin · Journal of Machine Learning Research | Dec 2005 | 1,412 |
| 26 | Smart manufacturing: Past research, present findings, and future directions Hyoung Seok Kang, Ju Yeon Lee, SangSu Choi et al. · International Journal of Precision Engineering and Manufacturing-Green Technology | 2016 | 1,373 |
| 27 | An End-to-End Steel Surface Defect Detection Approach via Fusing Multiple Hierarchical Features Yu He, Kechen Song, Qinggang Meng et al. · IEEE Transactions on Instrumentation and Measurement | 8 May 2019 | 1,338 |
| 28 | Transforming Auto-Encoders Geoffrey E. Hinton, Alex Krizhevsky, Sida D. Wang · Lecture notes in computer science | 2011 | 1,327 |
| 29 | Machine learning in manufacturing: advantages, challenges, and applications Thorsten Wuest, Daniel R. Weimer, Christopher Irgens et al. · Production & Manufacturing Research | 2016 | 1,307 |
| 30 | Predictive maintenance in the Industry 4.0: A systematic literature review Tiago Zonta, Cristiano André da Costa, Rodrigo da Rosa Righi et al. · Computers & Industrial Engineering | 6 Oct 2020 | 1,288 |
| 31 | YOLO-v1 to YOLO-v8, the Rise of YOLO and Its Complementary Nature toward Digital Manufacturing and Industrial Defect Detection Muhammad Hussain · Machines | 23 Jun 2023 | 1,194 |
| 32 | Smart Factory of Industry 4.0: Key Technologies, Application Case, and Challenges Baotong Chen, Jiafu Wan, Lei Shu et al. · IEEE Access | 14 Dec 2017 | 1,141 |
| 33 | Recent advances and trends in predictive manufacturing systems in big data environment Jay K. Lee, Edzel Lapira, Behrad Bagheri et al. · Manufacturing Letters | Oct 2013 | 1,071 |
| 34 | Exact Maximum A Posteriori Estimation for Binary Images D. M. Greig, B. T. Porteous, Allan H. Seheult · Journal of the Royal Statistical Society Series B (Statistical Methodology) | 1989 | 1,062 |
| 35 | Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems Vítor Alcácer, Virgílio António Cruz Machado · Engineering Science and Technology an International Journal | 31 Jan 2019 | 1,059 |
| 36 | Face recognition: the problem of compensating for changes in illumination direction Yael Adini, Yoram Moses, Shimon Ullman · IEEE Transactions on Pattern Analysis and Machine Intelligence | Jul 1997 | 1,058 |
| 37 | DRÆM – A discriminatively trained reconstruction embedding for surface anomaly detection Vitjan Zavrtanik, Matej Kristan, Danijel Skočaj · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) | Oct 2021 | 1,004 |
| 38 | A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects Kechen Song, Yunhui Yan · Applied Surface Science | 9 Sep 2013 | 992 |
| 39 | Learning from massive noisy labeled data for image classification Tong Xiao, Tian Xia, Yi Yang et al. | Jun 2015 | 970 |
| 40 | NB-CNN: Deep Learning-Based Crack Detection Using Convolutional Neural Network and Naïve Bayes Data Fusion Fu‐Chen Chen, Mohammad R. Jahanshahi · IEEE Transactions on Industrial Electronics | 19 Oct 2017 | 934 |
| 41 | Computer Vision - ECCV 2004 Thorsten Thormaehlen, Hellward Broszio, Axel Weißenfeld · Lecture notes in computer science | 2004 | 901 |
| 42 | Segmentation-based deep-learning approach for surface-defect detection Domen Tabernik, Samo Šela, Jure Skvarč et al. · Journal of Intelligent Manufacturing | 15 May 2019 | 883 |
| 43 | Color measurement in L∗a∗b∗ units from RGB digital images Katherine León, Domingo Mery, Franco Pedreschi et al. · Food Research International | 31 Mar 2006 | 883 |
| 44 | A review of the meanings and the implications of the Industry 4.0 concept Ana Pereira, Fernando Carlos Cabrita Romero · Procedia Manufacturing | 2017 | 877 |
| 45 | Contour tracking by stochastic propagation of conditional density Michael Isard, Andrew Blake · Lecture notes in computer science | 1996 | 865 |
| 46 | Industrial Artificial Intelligence for industry 4.0-based manufacturing systems Jay K. Lee, Hossein Seifi Davari, Jaskaran Singh et al. · Manufacturing Letters | 10 Sep 2018 | 862 |
| 47 | Clustering-based undersampling in class-imbalanced data Wei‐Chao Lin, Chih‐Fong Tsai, Ya‐Han Hu et al. · Information Sciences | 8 May 2017 | 842 |
| 48 | From Artificial Intelligence to Explainable Artificial Intelligence in Industry 4.0: A Survey on What, How, and Where Imran M Ahmed, Gwanggil Jeon, Francesco Piccialli · IEEE Transactions on Industrial Informatics | 27 Jan 2022 | 838 |
| 49 | Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0 Zeki Murat Çınar, Abubakar Abdussalam Nuhu, Qasim Zeeshan et al. · Sustainability | 5 Oct 2020 | 831 |
| 50 | A survey of edge detection techniques Larry Steven Davis · Computer Graphics and Image Processing | Sep 1975 | 812 |
Citations are OpenAlex's own counts, from its 2026-09-23 snapshot, so the ranking can differ from Google Scholar or Web of Science. A paper is listed for a field when OpenAlex files it under one of the field's topics; retracted papers and papers whose citation count cannot be traced to citing works are left out, and a preprint and the article it became are shown once. Release dates are OpenAlex's: where a source gave only a month or a year, that is what is shown. How the lists are chosen.