The development of machine vision and its applications in different industries: A review

Lili  Zhang; Xiaowei  Jia; Qing  Chang; Xin  Liu; Zhicheng  Zhang; Yanghao  Cao; Junjie  Liu; Yizhao  Yang

doi:10.59400/mea.v2i2.1746

Lili Zhang Machinery and Electronics Engineering College, Shandong Agriculture and Engineering University, Zibo 255300, China
Xiaowei Jia Machinery and Electronics Engineering College, Shandong Agriculture and Engineering University, Zibo 255300, China
Qing Chang Machinery and Electronics Engineering College, Shandong Agriculture and Engineering University, Zibo 255300, China
Xin Liu Machinery and Electronics Engineering College, Shandong Agriculture and Engineering University, Zibo 255300, China
Zhicheng Zhang Machinery and Electronics Engineering College, Shandong Agriculture and Engineering University, Zibo 255300, China
Yanghao Cao Machinery and Electronics Engineering College, Shandong Agriculture and Engineering University, Zibo 255300, China
Junjie Liu Ocean University of China, Qingdao 266100, China
Yizhao Yang Machinery and Electronics Engineering College, Shandong Agriculture and Engineering University, Zibo 255300, China

Article ID: 1746

3313 Views, 3068 PDF Downloads

DOI: https://doi.org/10.59400/mea.v2i2.1746

Keywords: machine vision; algorithm; applications; manufactruing

Abstract

In recent years, the development of machine vision research is rapid in several areas. In order to promote the better development of machine vision research, it is necessary to clarify its development and application direction. At present, there are few reviews on the application direction of machine vision. This paper sorts out the application of machine vision in various fields, and summarizes the current application status of machine vision from four main functions: recognition, measurement, classification and detection. This paper mainly introduces the improvement of different algorithms of machine vision and its application in medical, agriculture, manufacturing and other industries, providing guidance for the selection of machine vision research direction.

References

[1]Wu Mingfei, Chen Li, Zehuan Yao. Deep Active Learning for Computer Vision Tasks: Methodologies, Applications, and Challenges. Applied Sciences. 2022; 12(16): 8103.

[2]Katti H, Marius VP, Arun SP. Machine vision benefits from human contextual expectations. Scientific Reports. 2019; 9(1): 2112.

[3]Gorodokin V, Zhankaziev S, Shepeleva E, et al. Optimization of adaptive traffic light control modes based on machine vision. Transportation research procedia. 2021; 57: 241–249.

[4]Panagakis Y, Kossaifi J, Chrysos GG, et al. Tensor methods in computer vision and deep learning. Proceedings of the IEEE. 2021; 109(5): 863–890.

[5]Zhang D, Dongru H, Kang L, et al. The generative adversarial networks and its application in machine vision. Enterprise Information Systems. 2022; 16(2): 326–346.

[6]Gustafsson FK, Danelljan M, Schon TB. Evaluating scalable bayesian deep learning methods for robust computer vision. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops; 14–19 June 2020; Seattle, WA, USA.

[7]Whatmough PN., Zhou C, Hansen P, et al. Fixynn: Efficient hardware for mobile computer vision via transfer learning. Available online: https://doi.org/10.48550/arXiv.1902.11128 (accessed on 10 September 2024).

[8]Shu Y, Xiong C, Fan S. Interactive design of intelligent machine vision based on human–computer interaction mode. Microprocessors and microsystems. 2020; 75: 103059.

[9]Wu W, Li Q. Machine vision inspection of electrical connectors based on improved Yolo v3. IEEE Access. 2020; 8: 166184–166196.

[10]Wu P, He T, Zhu H, et al. Next‐generation machine vision systems incorporating two‐dimensional materials: progress and perspectives. InfoMat. 2022; 4(1): e12275.

[11]Reggiannini M, Moroni D. The use of saliency in underwater computer vision: A review. Remote Sensing. 2020; 13(1): 22.

[12]Akhtar N, Mian A, Kardan N, et al. Advances in adversarial attacks and defenses in computer vision: A survey. IEEE Access. 2021; 9: 155161–155196.

[13]Goel A, Tung C, Lu Y-H, et al. A survey of methods for low-power deep learning and computer vision. In: Proceedings of the 2020 IEEE 6th World Forum on Internet of Things (WF-IoT); 02–16 June 2020; New Orleans, LA, USA.

[14]O’Mahony N, Campbell S, Carvalho A, et al. Deep learning vs. traditional computer vision. In: Proceedings of the 2019 Computer Vision Conference (CVC); 25–26 April 2020; Las Vegas, Nevada, USA.

[15]Yang Z, Nahrstedt K, Guo H, et al. Deeprt: A soft real time scheduler for computer vision applications on the edge. In: Proceedings of the 2021 IEEE/ACM Symposium on Edge Computing (SEC); 14–17 December 2021; San Jose, CA, USA.

[16]Baygin M, Karakose M, Sarimaden A, et al. An image processing based object counting approach for machine vision application. Available online: https://doi.org/10.48550/arXiv.1802.05911 (accessed on 10 September 2024).

[17]Talebi Hossein, Milanfar P. Learning to resize images for computer vision tasks. In: Proceedings of the IEEE/CVF international conference on computer vision; 10–17 October 2021; Montreal, QC, Canada.

[18]El-Komy, A, Shahin OR, El-Aziz RM, et al. Integration of computer vision and natural language processing in multimedia robotics application. Inf. Sci. 2022; 11(3): 765–775.

[19]Li Jiali, Telychko M, Yin J, et al. Machine vision automated chiral molecule detection and classification in molecular imaging. Journal of the American Chemical Society. 2021; 143(27): 10177–10188.

[20]Hu Y, Yang S, Yang W, et al. Towards coding for human and machine vision: A scalable image coding approach. In: Proceedings of the 2020 IEEE International Conference on Multimedia and Expo (ICME); 6–10 July 2020; London, United Kingdom.

[21]Roggi G, Niccolai A, Grimaccia F, et al. A computer vision line-tracking algorithm for automatic UAV photovoltaic plants monitoring applications. Energies. 2020; 13(4): 838.

[22]Paul N, Chung C. Application of HDR algorithms to solve direct sunlight problems when autonomous vehicles using machine vision systems are driving into sun. Computers in Industry. 2018; 98: 192–196.

[23]Datta G, Liu Z, Yin Z, et al. Enabling ISPless Low-Power Computer Vision. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision; 2–7 January 2023; Waikoloa, HI, USA.

[24]Rebecq H, Ranftl R, Koltun V, et al. Events-to-video: Bringing modern computer vision to event cameras. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 7–11 January 2019; Waikoloa, HI, USA.

[25]Mennel L, Symonowicz J, Wachter S, et al. Ultrafast machine vision with 2D material neural network image sensors. Nature. 2020; 579(7797): 62–66.

[26]Gamanayake C, Jayasinghe L, Ng B, et al. Cluster pruning: An efficient filter pruning method for edge AI vision applications. IEEE Journal of Selected Topics in Signal Processing. 2020; 14(4): 802–816.

[27]Cong VD, Hanh LD, Phuong LH, et al. Design and development of robot arm system for classification and sorting using machine vision. FME Transactions. 2022; 50(1): 181–181.

[28]Gubbi MR, Gonzalez EA, Lediju Bell MA. Theoretical framework to predict generalized contrast-to-noise ratios of photoacoustic images with applications to computer vision. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 2022; 69(6): 2098–2114.

[29]Wang S, Wang C-Y, Wang P, et al. Networking retinomorphic sensor with memristive crossbar for brain-inspired visual perception. National science review. 2021; 8(2): nwaa172.

[30]Tarashima S, Center I. One-Shot Deep Model for End-to-End Multi-Person Activity Recognition. British Machine Vision Conference. 2021.

[31]Shreyas E, Sheth MH, Mohana. 3D Object Detection and Tracking Methods using Deep Learning for Computer Vision Applications. In: Proceedings of the 2021 International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT); 27–28 August 2021; Bangalore, India.

[32]Ding J, Zhang Z, Yu X, et al. A Novel Moving Object Detection Algorithm Based on Robust Image Feature Threshold Segmentation with Improved Optical Flow Estimation. Applied Sciences. 2023; 13(8): 4854.

[33]Fang S, Zhang B, Hu J. Improved mask R-CNN multi-target detection and segmentation for autonomous driving in complex scenes. Sensors. 2023; 23(8): 3853.

[34]Moru DK, Borro D. A machine vision algorithm for quality control inspection of gears. The International Journal of Advanced Manufacturing Technology. 2020; 106: 105–123.

[35]Li J, Mengu D, Yardimci NT, et al. Spectrally encoded single-pixel machine vision using diffractive networks. Science Advances. 2021; 7(13): eabd7690.

[36]Yin K, Wang L, Zhang J. ST-CSNN: a novel method for vehicle counting. Machine Vision and Applications. 2021; 32(5): 108.

[37]Dan D, Ge L, Yan X. Identification of moving loads based on the information fusion of weigh-in-motion system and multiple camera machine vision. Measurement. 2019; 144: 155–166.

[38]Appiah O, Asante M, Hayfron-Acquah BJ. Improved approximated median filter algorithm for real-time computer vision applications. Journal of King Saud University-Computer and Information Sciences. 2022; 34(3): 782–792.

[39]Kaushal V, Iyer R, Kothawade S, et al. Learning from less data: A unified data subset selection and active learning framework for computer vision. In: Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV); 7–11 January 2019; Waikoloa, HI, USA.

[40]Zhang M, Zhe X, Ou-Yang L, et al. Semantic hierarchy preserving deep hashing for large-scale image retrieval. In: Proceedings of the 2021 17th International Conference on Machine Vision and Applications (MVA); 25–27 July 2021; Aichi, Japan.

[41]Semitsu T, Nakamura M, Ishigami S, et al. Estimating Contribution of Training Datasets using Shapley Values in Data-scale for Visual Recognition. In: Proceedings of the 2021 17th International Conference on Machine Vision and Applications (MVA); 25–27 July 2021; Aichi, Japan.

[42]Riba E, Mishkin D, Ponsa D, et al. Kornia: An open source differentiable computer vision library for pytorch. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision; 13–19 June 2020; Seattle, WA, USA.

[43]Yang S, Hu Y, Yang W, et al. Towards coding for human and machine vision: Scalable face image coding. IEEE Transactions on Multimedia. 2021; 23: 2957–2971.

[44]Suma V. Computer vision for human-machine interaction-review. Journal of trends in Computer Science and Smart technology (TCSST). 2019; 1(02): 131–139.

[45]Tu K, Li L, Yang L, et al. Selection for high quality pepper seeds by machine vision and classifiers. Journal of Integrative Agriculture. 2018; 17(9): 1999–2006.

[46]Ansari N, Ratri SS, Jahan A, et al. Inspection of paddy seed varietal purity using machine vision and multivariate analysis. Journal of Agriculture and Food Research. 2021; 3: 100109.

[47]Tu K, Wen S, Cheng Y, et al. A non-destructive and highly efficient model for detecting the genuineness of maize variety ‘JINGKE 968’ using machine vision combined with deep learning. Computers and Electronics in Agriculture. 2021; 182: 106002.

[48]Xu P, Tan Q, Zhang Y, et al. Research on maize seed classification and recognition based on machine vision and deep learning. Agriculture. 2022; 12(2): 232.

[49]Iraji M. Comparison between soft computing methods for tomato quality grading using machine vision. Journal of Food Measurement and Characterization. 2019; 13(1): 1–15.

[50]Kumar SD, Esakkirajan S, Bama S, et al. A microcontroller based machine vision approach for tomato grading and sorting using SVM classifier. Microprocessors and Microsystems. 2020; 76: 103090.

[51]Liu L, Li Z, Shi Y, et al. Design of a tomato classifier based on machine vision. PloS one. 2019; 14(7): e0219803.

[52]Belan PA, de Macedo RAG, Luz Alves WA, et al. Machine vision system for quality inspection of beans. The International Journal of Advanced Manufacturing Technology. 2020; 111: 3421–3435.

[53]Zhou L, Chalana V, Kim Y. PC‐based machine vision system for real‐time computer‐aided potato inspection. International journal of imaging systems and technology. 1998; 9(6): 423–433.

[54]Thinh NT, Duc Thong N, Cong HT, et al. Mango classification system based on machine vision and artificial intelligence. In: Proceedings of the 2019 7th International Conference on Control, Mechatronics and Automation (ICCMA); 6–8 November 2019; Delft, Netherlands.

[55]Ismail N, Malik OA. Real-time visual inspection system for grading fruits using computer vision and deep learning techniques. Information Processing in Agriculture. 2022; 9(1): 24–37.

[56]Uluişik S, Yildiz F, Özdemİr AT. Image processing based machine vision system for tomato volume estimation. In: Proceedings of the 2018 Electric Electronics, Computer Science, Biomedical Engineerings’ Meeting (EBBT); 18–19 April 2018; Istanbul, Turkey.

[57]Nyalala I, Okinda C, Chao Q, et al. Weight and volume estimation of single and occluded tomatoes using machine vision. International Journal of Food Properties. 2021; 24(1): 818–832.

[58]Concha-Meyer A, Eifert J, Wang H, et al. Volume estimation of strawberries, mushrooms, and tomatoes with a machine vision system. International Journal of Food Properties. 2018; 21(1): 1867–1874.

[59]Vrochidou E, Banzinas C, Manios M, et al. Machine vision for ripeness estimation in viticulture automation. Horticulturae. 2021; 7(9): 282.

[60]Sabzi S, Nadimi M, Abbaspour-Gilandeh Y, et al. Non-destructive estimation of physicochemical properties and detection of ripeness level of apples using machine vision. International Journal of Fruit Science. 2022; 22(1): 628–645.

[61]Septiarini A, Sunyoto A, Hamdani H, et al. Machine vision for the maturity classification of oil palm fresh fruit bunches based on color and texture features. Scientia Horticulturae. 2021; 286: 110245.

[62]Kamrul MH, Paul P, Rahman M. Machine vision based rice disease recognition by deep learning. 2019 22nd International Conference on Computer and Information Technology (ICCIT). IEEE, 2019.

[63]Mahmud MS, Zaman QU, Esau TJ, et al. Development of an artificial cloud lighting condition system using machine vision for strawberry powdery mildew disease detection. Computers and Electronics in Agriculture. 2019; 158: 219–225.

[64]Kim W-S, Lee D-H, Kim Y-J. Machine vision-based automatic disease symptom detection of onion downy mildew. Computers and Electronics in Agriculture. 2020; 168: 105099.

[65]Palei S, Behera SK, Sethy PK. A Systematic Review of Citrus Disease Perceptions and Fruit Grading Using Machine Vision. Procedia Computer Science. 2023; 218: 2504–2519.

[66]Martínez-Heredia JM, Gálvez AI, Colodro F, et al. Feasibility Study of Detection of Ochre Spot on Almonds Aimed at Very Low-Cost Cameras Onboard a Drone. Drones. 2023; 7(3): 186.

[67]Jiang H, Li X, Safara F. IoT-based agriculture: Deep learning in detecting apple fruit diseases. Microprocessors and Microsystems. 2021; 104321.

[68]Lins EA, Mazuco Rodriguez JP, Scoloski SI, et al. A method for counting and classifying aphids using computer vision. Computers and electronics in agriculture. 2020; 169: 105200.

[69]Liu H, Chahl JS. A multispectral machine vision system for invertebrate detection on green leaves. Computers and Electronics in Agriculture. 2018; 150: 279–288.

[70]Rani SVJ, Kumar PS, Priyadharsini R, et al. Automated weed detection system in smart farming for developing sustainable agriculture. International Journal of Environmental Science and Technology. 2022; 19(9): 9083–9094.

[71]Abouzahir S, Sadik M, Sabir E. Enhanced approach for weeds species detection using machine vision. In: Proceedings of the 2018 International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS); 5–6 December 2018; Kenitra, Morocco.

[72]Li N, Zhang X, Zhang C, et al. Review of machine-vision-based plant detection technologies for robotic weeding. In: Proceedings of the 2019 IEEE International Conference on Robotics and Biomimetics (ROBIO); 6–8 December 2019; Dali, China.

[73]Wu Z, Chen Y, Zhao B, et al. Review of weed detection methods based on computer vision. Sensors. 2021; 21(11): 3647.

[74]Guo Y, He D, Chai L. A machine vision-based method for monitoring scene-interactive behaviors of dairy calf. Animals. 2020; 10(2): 190.

[75]Guo Y, Chai L, Aggrey SE, et al. A machine vision-based method for monitoring broiler chicken floor distribution. Sensors. 2020; 20(11): 3179.

[76]Zhang S, Yang X, Wang Y, et al. Automatic fish population counting by machine vision and a hybrid deep neural network model. Animals. 2020; 10(2): 364.

[77]Zhou K, Meng Z, He M, et al. Design and test of a sorting device based on machine vision. IEEE Access. 2020; 8: 27178–27187.

[78]Kanagasingham S, Ekpanyapong M, Chaihan R. Integrating machine vision-based row guidance with GPS and compass-based routing to achieve autonomous navigation for a rice field weeding robot. Precision Agriculture. 2020; 21(4): 831–855.

[79]Terra FP, do Nascimento GH, Duarte GA, et al. Autonomous agricultural sprayer using machine vision and nozzle control. Journal of Intelligent & Robotic Systems. 2021; 102(2): 38.

[80]Bai J, Hao F, Cheng G, et al. Machine vision-based supplemental seeding device for plug seedling of sweet corn. Computers and Electronics in Agriculture. 2021; 188: 106345.

[81]Williams HAM, Jone MH, Nejati M, et al. Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms. Biosystems Engineering. 2019; 181: 140–156.

[82]Wood DA., Kafiabadi S, Busaidi AA, et al. Deep learning to automate the labelling of head MRI datasets for computer vision applications. European radiology. 2022; 32: 725–736.

[83]Zhao X. A Computer Vision System for Missing Tablets Detection. Journal of Physics: Conference Series. 2021; 1827(1).

[84]Sethy PK, Pandey C, Khan MR, et al. A cost-effective computer-vision based breast cancer diagnosis. Journal of Intelligent & Fuzzy Systems. 2021; 41(5): 5253–5263.

[85]Alkadi R, Taher F, El-Baz A, et al. A deep learning-based approach for the detection and localization of prostate cancer in T2 magnetic resonance images. Journal of digital imaging. 2019; 32: 793–807.

[86]Gu Y, Pandit S, Saraee E, et al. Home-based physical therapy with an interactive computer vision system. In: Proceedings of the IEEE/CVF International Conference on Computer Vision Workshops; 27–28 October 2019; Seoul, Korea (South).

[87]Ghani A, Aina A, See CH, et al. Accelerated diagnosis of novel Coronavirus (COVID-19)—Computer vision with convolutional neural networks (CNNs). Electronics. 2022; 11(7): 1148.

[88]Kollias D, Arsenos A, Kollias S. Ai-mia: Covid-19 detection and severity analysis through medical imaging. In: Proceedings of the European Conference on Computer Vision; 23–27 October 2022; Tel Aviv, Israel.

[89]Ismael SAA, Mohammed A, Hefny H. An enhanced deep learning approach for brain cancer MRI images classification using residual networks. Artificial intelligence in medicine. 2020; 102: 101779.

[90]Gu Y, Yang J. Application of computer vision and deep learning in breast cancer assisted diagnosis. In: Proceedings of the 3rd International Conference on Machine Learning and Soft Computing; 25–28 January 2019; Da Lat, Viet Nam.

[91]Khemasuwan D, Sorensen JS, Colt HG. Artificial intelligence in pulmonary medicine: Computer vision, predictive model and COVID-19. European respiratory review. 2020; 29(157).

[92]Welikala RA, Remagnino P, Lim JH, et al. Automated detection and classification of oral lesions using deep learning for early detection of oral cancer. IEEE Access. 2020; 8: 132677–132693.

[93]Azizi S, Mustafa B, Ryan F, et al. Big self-supervised models advance medical image classification. In: Proceedings of the IEEE/CVF international conference on computer vision; 10–17 October 2021; Montreal, QC, Canada.

[94]Yeung S, Downing NL, Fei-Fei L, et al. Bedside computer vision-moving artificial intelligence from driver assistance to patient safety. N Engl J Med. 2018; 378(14): 1271–1273.

[95]Aksac A, Demetrick DJ, Ozyer T, Alhajj R. BreCaHAD: A dataset for breast cancer histopathological annotation and diagnosis. BMC research notes. 2019; 12(1): 1–3.

[96]Zhou Y, Graham S, Koohbanani NA, et al. Cgc-net: Cell graph convolutional network for grading of colorectal cancer histology images. In: Proceedings of the IEEE/CVF international conference on computer vision workshops; 27–28 October 2019; Seoul, Korea (South).

[97]Chao S, Belanger D. Generalizing Few-Shot Classification of Whole-Genome Doubling Across Cancer Types. In: Proceedings of the IEEE/CVF International Conference on Computer Vision; 10–17 October 2021; Montreal, QC, Canada.

[98]Chen X, Williams BM, Vallabhaneni SR, et al. Learning active contour models for medical image segmentation. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 15–20 June 2019; Long Beach, CA, USA.

[99]Shah NA, Gupta D, Lodaya R, et al. Colorectal cancer segmentation using atrous convolution and residual enhanced unet. In: Proceedings of the Computer Vision and Image Processing: 5th International Conference, CVIP 2020; 4–6 December 2020; Prayagraj, India.

[100]Ulhaq A, Khan A, Gomes D, et al. Computer vision for COVID-19 control: A survey. Available online: https://doi.org/10.48550/arXiv.2004.09420 (accessed on 10 September 2024).

[101]Rehman KU, Li J, Pei Y, et al. Computer vision-based microcalcification detection in digital mammograms using fully connected depthwise separable convolutional neural network. Sensors. 2021; 21(14): 4854.

[102]Abdelrahman L, Ghamdi MA, Collado-Mesa F, et al. Convolutional neural networks for breast cancer detection in mammography: A survey. Computers in biology and medicine. 2021; 131: 104248.

[103]Rakhlin A, Shvets A, Iglovikov V, et al. Deep convolutional neural networks for breast cancer histology image analysis. In: Proceedings of the Image Analysis and Recognition: 15th International Conference, ICIAR 2018; 27–29 June 2018; Póvoa de Varzim, Portugal.

[104]Haskins G, Kruger U, Yan P. Deep learning in medical image registration: a survey. Machine Vision and Applications. 2020; 31: 1–18.

[105]Esteva A, Chou K, Yeung S, et al. Deep learning-enabled medical computer vision. NPJ digital medicine. 2021; 4(1): 5. 25–1

[106]Zheng J, Lin D, Gao Z, et al. Deep learning assisted efficient AdaBoost algorithm for breast cancer detection and early diagnosis. IEEE Access. 2020; 8: 96946–96954.

[107]Amiri E, Roozbakhsh Z, Amiri S, et al. Detection of topographic images of keratoconus disease using machine vision. International Journal of Engineering Science and Application. 2020; 4(4): 145–150.

[108]Wu J-X, Liu H-C, Chen P-Y, et al. Enhancement of ARFI-VTI elastography images in order to preliminary rapid screening of benign and malignant breast tumors using multilayer fractional-order machine vision classifier. IEEE access. 2020; 8: 164222–164237.

[109]Gupta V, Bhavsar A. Partially-independent framework for breast cancer histopathological image classification. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops; 15–20 June 2019; Long Beach, CA, USA.

[110]Gupta V, Bhavsar A. Sequential modeling of deep features for breast cancer histopathological image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops; 18–22 June 2018; Salt Lake City, UT, USA.

[111]Sheikh TS, Lee Y, Cho M. Histopathological classification of breast cancer images using a multi-scale input and multi-feature network. Cancers. 2020; 12(8): 2031.

[112]Liu Z, Xu X, Liu T, et al. Machine vision guided 3d medical image compression for efficient transmission and accurate segmentation in the clouds. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition; 15–20 June 2019; Long Beach, CA, USA.

[113]Harned Z, Lungren MP, Rajpurkar P. Machine vision, medical AI, and malpractice. JL & Tech. Dig. 2019.

[114]Mok TCW, Chung A. Affine medical image registration with coarse-to-fine vision transformer. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition; 16–22 June 2024; New Orleans, LA, USA.

[115]Wu J-X, Chen P-Y, Chen P-Y, et al. Multilayer fractional-order machine vision classifier for rapid typical lung diseases screening on digital chest X-ray images. IEEE Access. 2020; 8: 105886–105902.

[116]Yoo S, Gujrathi I, Haider MA, et al. Prostate cancer detection using deep convolutional neural networks. Scientific reports. 2019; 9(1): 19518.

[117]Hassan H, Ren Z, Zhao H, et al. Review and classification of AI-enabled COVID-19 CT imaging models based on computer vision tasks. Computers in biology and medicine. 2022; 141: 105123.

[118]Zhang Ni, Cai Y-X, Wang Y-Y, et al. Skin cancer diagnosis based on optimized convolutional neural network. Artificial intelligence in medicine. 2020; 102: 101756.

[119]Deniz E, Şengür A, Kadiroğlu Z, et al. Transfer learning based histopathologic image classification for breast cancer detection. Health information science and systems. 2018; 6: 1–7.

[120]Zhao T, Zhang H, Spoelstra J. A computer vision application for assessing facial acne severity from selfie images. Available online: https://doi.org/10.48550/arXiv.1907.07901 (accessed on 10 September 2024).

[121]Yu H, Liang Y, Liang H, et al. Recognition of wood surface defects with near infrared spectroscopy and machine vision. Journal of Forestry Research. 2019; 30(6): 2379–2386.

[122]Chen S, Lai W, Ye J, et al. A Fast and Low-Power Detection System for the Missing Pin Chip Based on YOLOv4-Tiny Algorithm. Sensors. 2023; 23(8): 3918.

[123]Lin X, Wang X, Li L. Intelligent detection of edge inconsistency for mechanical workpiece by machine vision with deep learning and variable geometry model. Applied Intelligence. 2020; 50: 2105–2119.

[124]Liong S-T, Gan YS, Huang Y-C, et al. Integrated neural network and machine vision approach for leather defect classification. Available online: https://doi.org/10.48550/arXiv.1905.11731 (accessed on 10 September 2024).

[125]Lin Z, Lai Y, Pan T, et al. A new method for automatic detection of defects in selective laser melting based on machine vision. Materials. 2021; 14(15): 4175.

[126]Ryu S-G, Kim SW, Yun JP, et al. Detection of scarfing faults on the edges of slabs. ISIJ international. 2014; 54(1): 112–118.

[127]Massinaei M, Jahedsaravani A, Taheri E, et al. Machine vision based monitoring and analysis of a coal column flotation circuit. Powder Technology. 2019; 343: 330–341.

[128]Wang J, Lee S. Data augmentation methods applying grayscale images for convolutional neural networks in machine vision. Applied Sciences. 2021; 11(15): 6721.

[129]Jain S, Seth G, Paruthi A. Synthetic data augmentation for surface defect detection and classification using deep learning. Journal of Intelligent Manufacturing. 2022; 1–14.

[130]Hao R, Lu B, Cheng Y, et al. A steel surface defect inspection approach towards smart industrial monitoring. Journal of Intelligent Manufacturing. 2021; 32: 1833–1843.

[131]Zhang J, Kang X, Ni H, et al. Surface defect detection of steel strips based on classification priority YOLOv3-dense network. Ironmaking & Steelmaking. 2021; 48(5): 547–558.

[132]Boikov A, Payor V, Savelev R, et al. Synthetic data generation for steel defect detection and classification using deep learning. Symmetry. 2021; 13(7): 1176.

[133]Penumuru DP, Muthuswamy S, Karumbu P. Identification and classification of materials using machine vision and machine learning in the context of industry 4.0. Journal of Intelligent Manufacturing. 2020; 31(5): 1229–1241.

[134]Benbarrad T, Salhaoui M, Kenitar SB, et al. Intelligent machine vision model for defective product inspection based on machine learning. Journal of Sensor and Actuator Networks. 2021; 10(1): 7.

[135]Peng R, Liu J, Fu X, et al. Application of machine vision method in tool wear monitoring. The International Journal of Advanced Manufacturing Technology 116.3-4. 2021; 1357–1372.

[136]Dai W, Li D, Tang D, et al. Deep learning assisted vision inspection of resistance spot welds. Journal of Manufacturing Processes. 2021; 62: 262–274.

[137]Zhang Z, Liu Y, Hu Q, et al. Multi-information online detection of coal quality based on machine vision. Powder Technology. 2020; 374: 250–262.

[138]Sikander G, Anwar S. A novel machine vision-based 3d facial action unit identification for fatigue detection. IEEE Transactions on Intelligent Transportation Systems. 2020; 22(5): 2730–2740.

[139]Zhao R, Jia P, Wang Y, et al. Dynamic model of crowd flow in multi-angle cross passages based on machine vision. In: Proceedings of the 2021 IEEE 5th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC); 12–14 March 2021; Chongqing, China.

[140]Arellano-González JC, Medellín-Castillo HI, Hernández-Molina RI, et al. Determination of Anthropometric Lengths of Body Segments Using Machine Vision Systems. Machines. 2023; 11(3): 369.

[141]Panahi L, Ghods V. Human fall detection using machine vision techniques on RGB–D images. Biomedical Signal Processing and Control. 2018; 44: 146–153.

[142]Huang H, Moaveni M, Schmidt S, et al. Evaluation of Railway Ballast Permeability Using Machine Vision–Based Degradation Analysis. Transportation Research Record. 2018; 2672(10): 62–73.

[143]Burghardt TE, Popp R, Helmreich B, et al. Visibility of various road markings for machine vision. Case Studies in Construction Materials. 2021; 15: e00579.

[144]Yang H, Wang Y, Hu J, et al. Deep learning and machine vision-based inspection of rail surface defects. IEEE Transactions on Instrumentation and Measurement. 2021; 71: 1–14.

[145]Dan D, Ying Y, Ge L. Digital Twin System of Bridges Group Based on Machine Vision Fusion Monitoring of Bridge Traffic Load. IEEE transactions on intelligent transportation systems. 2021; 23.

[146]Dan D, Dan Q. Automatic recognition of surface cracks in bridges based on 2D-APES and mobile machine vision. Measurement. 2021; 168.

[147]Liu Z, He Y, Wang C, et al. Analysis of the influence of foggy weather environment on the detection effect of machine vision obstacles. Sensors. 2020; 20(2): 349.

[148]Kim H, Han J, Han TY-J. Machine vision-driven automatic recognition of particle size and morphology in SEM images. Nanoscale. 2020; 12(37): 19461–19469.

[149]Nartova AV., Mashukov MY, Astakhov RR, et al. Particle recognition on transmission electron microscopy images using computer vision and deep learning for catalytic applications. Catalysts. 2022; 12(2): 135.

[150]Hendel D, Johnston KV, Patra RK, et al. A machine-vision method for automatic classification of stellar halo substructure. Monthly Notices of the Royal Astronomical Society. 2019; 486(3): 3604–3616.