Machine learning-based imaging system for surface defect inspection

Gradient-based learning applied to document recognition

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A fast learning algorithm for deep belief nets

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Defect detection in textured materials using Gabor filters

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Convolutional Neural Networks for Steel Surface Defect Detection from Photometric Stereo Images

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Effective image models for inspecting profile flaws of car mirrors with applications

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Learnings graph-Fourier spectra of textured surface images for defect localization

Nakkina, T. G., Karthikeyan, A., Zhong, Y., Eksin, C., & Bukkapatnam, S. (2024) · Manufacturing Letters · doi:10.1016/j.mfglet.2024.09.183 · Cited by 3

Sheet metal localization using deep learning and synthetic data

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Classification of clay bricks using vision-based machine learning algorithms

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YSCANet: a lightweight multi-scale self-calibrating feature fusion network for wafer defect detection

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Cyber-enabled distributed machine learning for smart manufacturing systems

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Defect Detection of the Irregular Turbine Blades Based on Edge Pixel Direction Information

Li-Yong, M., Chun-Sheng, H., & He, Y. (2018) · International Conference on Network and Information Systems for Computers (ICNISC) · doi:10.1109/icnisc.2018.00040 · Cited by 3

On study of a method for detecting micro-deformation defects of steel plate surface

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Design of an optical linear discriminant filter for classification of subwavelength concave and convex defects on dielectric substrates

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Taguchi Based Design of Sequential Convolution Neural Network for Classification of Defective Fasteners

Kaur, M., Chauhan, K. K., Aggarwal, T., Bharadwaj, P., Vig, R., Ihianle, I. K., Joshi, G., & Owa, K. (2023) · Advances in intelligent systems and computing · doi:10.1007/978-981-19-5443-6_39 · Cited by 3

Gerçek Zamanlı Kusur Tespiti: LPG Tüplerinin Yüzeylerinde Kirlilikleri Tanımlama için Görüntü İşleme ve Makine Öğrenimi Teknikleri ile Yenilikçi Bir Yaklaşım

Özcan, H., Gençtürk, T. H., Genç, G., Yildirim, T., Durmuş, F., & Gürleyen, A. (2024) · Afyon Kocatepe University Journal of Sciences and Engineering · doi:10.35414/akufemubid.1364153 · Cited by 3

Metal Surface Defect Detection Based on Weighted Fusion

Wang, C., Xu, J., Liang, X., & Yin, D. (2020) · International Conference on Virtual Reality and Visualization · doi:10.1109/icvrv51359.2020.00044 · Cited by 3

Deep Learning for Semiconductor Defect Classification

Sweeney, T., Coleman, S., & Kerr, D. (2022) · IEEE International Conference on Industrial Informatics (INDIN) · doi:10.1109/indin51773.2022.9976162 · Cited by 3

Image Inspection of Railcar Structural Components: An approach through Deep Learning and Discrete Fourier Transform

Rocha, R. D. L., Silva, C. D., Gomes, A. C., Ferreira, B. V., Carvalho, E. C., Siravenha, A. C., & Carvalho, S. R. (2019) · doi:10.5753/kdmile.2019.8786 · Cited by 3

Automatic Internal Wrinkles Detection of Lithium-ion Batteries using Convolutional Neural Network

Peng, J.-W., Xue, M., & Lou, Y. (2021) · IEEE International Conference on Automation Science and Engineering (CASE) · doi:10.1109/case49439.2021.9551649 · Cited by 3

Defects Clustering for Mineral Sintering Surface Based on Multi-source Data Fusion

Yang, Y., Chen, T., Zhao, L., Gu, J., Tang, X., & Zhang, Y. (2023) · Conference on Fully Actuated System Theory and Applications (FASTA) · doi:10.1109/cfasta57821.2023.10243223 · Cited by 3

A Real Time Intelligent Image Based Document Classification Using CNN and SVMPreprint

Potharaju, S. P., Tambe, S. N., & Tambe, S. B. (2023) · Research Square · doi:10.21203/rs.3.rs-3409814/v1 · Cited by 3

Automated Visual Inspection

Deepthi, M. K., Abhishek Reddy, P., & Abhishek Reddy, P. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-15365 · Cited by 3

Quantitative detection for textured surface cracks based on cluster integration

Xu, B., Wu, Y., Li, J., Zhang, B., & Liu, K. (2023) · Applied Optics · doi:10.1364/ao.482260 · Cited by 2

Deep Learning based Defect Detection Algorithm for Solar Panels

Li, J., Li, H., Wu, Y., Zhou, H., Manfredi, L., Li, P., & Zhang, H. (2023) · WRC Symposium on Advanced Robotics and Automation (WRC SARA) · doi:10.1109/wrcsara60131.2023.10261859 · Cited by 2

Visual Geometry Group Network for Flexible Printed Circuit Board Surface Defect Classification

Sheng, J., Guo, S., Li, H., Shen, S., Zhang, Y., Huang, Y., Sun, B., & Wang, J. (2024) · Canadian Journal of Electrical and Computer Engineering · doi:10.1109/icjece.2024.3368454 · Cited by 3

Environmentally-Robust Defect Classification With Domain Augmentation Framework

Lee, S., & Shim, J. (2024) · IEEE Access · doi:10.1109/access.2024.3453371 · Cited by 2

Defect Detection in Freight Trains Using a Lightweight and Effective Multi-Scale Fusion Framework with Knowledge Distillation

Ma, Z., Zhou, S., & Lin, C. (2025) · Electronics · doi:10.3390/electronics14050925 · Cited by 2

A machine vision system for inspecting mechanical parts

Rajagounder, R. (2025) · Machine Graphics and Vision · doi:10.22630/mgv.2025.34.1.4 · Cited by 2

A Data-Driven Machine Vision Framework for Quality Management in Photovoltaic Module Manufacturing

Lee, I.-J., Kim, Y., & Kim, S. (2025) · Machines · doi:10.3390/machines13040285 · Cited by 2

Enhancing Generalization of Wafer Defect Detection by Data Discrepancy-aware Preprocessing and Contrast-varied Augmentation

Yang, C., Hai, L., Chen, Y., & Hu, J. (2020) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/asp-dac47756.2020.9045391 · Cited by 2

Baru-Net: Surface Defects Detection of Highly Reflective Chrome-Plated Appearance Parts

Chen, J., Zhang, B., Jiang, Q., & Chen, X. (2023) · Coatings · doi:10.3390/coatings13071205 · Cited by 2

Identification and localization of veneer knot defects based on parallel structure fusion approach

Zhong, L., Dai, Z., Zhang, Z., Sun, Y., Cao, Y., & Wang, L. (2024) · European Journal of Wood and Wood Products · doi:10.1007/s00107-024-02086-y · Cited by 2

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

Optimized YOLO and Semi-Supervised Fuzzy Graph Convolutional Network (SSFGCN) For Surface Defect Recognition

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

Artificial Auditory Perception Pattern Recognition System Based on Spatiotemporal Convolutional Neural Network

Fang, X., Fang, H., Feng, Z. Z., Wang, J., & Zhou, L. (2019) · Applied Sciences · doi:10.3390/app10010139 · Cited by 2

Camera module Lens blemish detection based on neural network interpretability

Yang, M., Wu, J. Y., & Niu, X. (2021) · Multimedia Tools and Applications · doi:10.1007/s11042-021-11716-z · Cited by 2

Hemispherical Surface Defect Classification of Valve Core with a Noval Semi-supervised Learning

Hao, L., Wu, X., Li, C., & Zhang, W. (2022) · Recent Patents on Mechanical Engineering · doi:10.2174/2212797615666220204112342 · Cited by 2

Detection of Prostate Cancer Using MRI Images Classification with Deep Learning Techniques

Mosleh, M. A. A., Hamoud, M. H. A., & Alsabri, A. A. (2022) · International Conference on Emerging Smart Technologies and Applications (eSmarTA) · doi:10.1109/esmarta56775.2022.9935491 · Cited by 2

Imbalanced Data Classification with Fuzzy Logic and Universal Image Fusion for Gearbox Defect Detection

Barshooi, A. H., Yazdanijoo, A., Bagheri, E., & Moosavian, A. (2024) · CSI International Symposium on Artificial Intelligence and Signal Processing · doi:10.1109/aisp61396.2024.10475291 · Cited by 2

Automated Detection and Classification of Defects in Pcb Using Deep Learning Techniques: A Comparison of Resnet50v1 and Efficient Det D1

Sharma, S., Patel, D., & Shah, D. J. (2023) · IEEE India International Conference on Power Electronics · doi:10.1109/iicpe60303.2023.10475108 · Cited by 3

Surface Defect Detection of Remanufactured Products by Using the Improved Yolov5

Sun, W., Liu, Z., Wang, Q., & Zhu, B. (2024) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-52649-7_19 · Cited by 2

BitFlow-Net: Toward Fully Binarized Convolutional Neural Networks

Wu, L., Jiang, P., Chen, Z., Lin, X., Lai, Y., Lin, P., & Cheng, S. (2019) · IEEE Access · doi:10.1109/access.2019.2945488 · Cited by 2

Two-step Convolutional Neural Network for Image Defect Detection

Mei, Z., Wu, J., Yuan, P., & Zhu, J. (2019) · Chinese Control Conference (CCC) · doi:10.23919/chicc.2019.8866625 · Cited by 2

ProQualAI: A software to compute qualitative and quantitative product features in process industries using deep learning image processing algorithms

Nieto, M. T. A., Gelbhardt, H., Ohlendorf, J.-H., & Thoben, K. (2024) · SoftwareX · doi:10.1016/j.softx.2024.101631 · Cited by 2

WITHDRAWN: EMAT-Based Crack Detection in Railway Tracks Using Multi-Domain Signal Processing and Scalogram-Driven Deep LearningPreprint

Asif, M. T., Malik, M. S., Khan, A. A., Ali, N., Iqbal, A., & Huang, B. (2025) · Research Square · doi:10.21203/rs.3.rs-6005043/v1 · Cited by 2

Study on Microscopic Fracture Surface Analysis based on Deep Learning (1) : Fracture Surface Classification by Convolutional Neural Network

Lee, S., & Choi, S.-D. (2022) · Journal of the Korean Society of Manufacturing Process Engineers · doi:10.14775/ksmpe.2022.21.12.001 · Cited by 1

Faster and lighter surface defect detection model for transparent wine bottle

Lv, Z., Gu, G., Xia, K., Zhao, Z., Liu, K., Yin, L., Xing, L., & Ying, L. (2025) · International Journal of Machine Learning and Cybernetics · doi:10.1007/s13042-024-02459-y · Cited by 1

Cognitive outline study of Prostate Cancer through Machine Learning Algorithms

Shaurya, R., Sharma, K., Tiwari, S., Vijh, S., & Jadon, J. S. (2025) · Procedia Computer Science · doi:10.1016/j.procs.2025.03.353 · Cited by 1

Intelligent Detection of Passive and Active Components in PCB Imagery Using YOLOv12 for Automated Inspection Systems

Bakırcı, M., & Bayraktar, I. (2025) · International Russian Automation Conference (RusAutoCon) · doi:10.1109/rusautocon65989.2025.11177309 · Cited by 1

A Detection Frame of Metal Surface Defect Based on Improved Deformable-DETR

Zhao, C., & Wang, Y. (2024) · doi:10.1109/ictech63197.2024.00079 · Cited by 1

A Two-Stage High-Precision Recognition and Localization Framework for Key Components on Industrial PCBs

Wang, L., Ouyang, L., Weng, H., Chen, X., Wang, A., & Zhang, K. (2025) · Mathematics · doi:10.3390/math14010004 · Cited by 1

Quality Inspection Experimentation on Image Processing with CPUs and GPUs: A Case Study

Zhang, Y., Buno, R. R., Lin, C.-Y., Tsai, Y.-K., & Allen, T. T. (2026) · Lecture notes on multidisciplinary industrial engineering · doi:10.1007/978-981-96-8323-9_19 · Cited by 1

Deep learning in wood image analysis: a systematic review of techniques, challenges, and trends (2015–2025)

Duc, M. L., & Bilík, P. (2026) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-025-02779-y · Cited by 1

USING METHOD OF MACHINE TRAINING AND ARTIFICIAL INTELLIGENCE IN CHEMICAL TECHNOLOGY. PART I

Nigmatullin, V. R., & Rudnev, N. A. (2019) · Oil and Gas Business · doi:10.17122/ogbus-2019-4-243-268 · Cited by 3

USING METHOD OF MACHINE TRAINING AND ARTIFICIAL INTELLIGENCE IN CHEMICAL TECHNOLOGY. PART II

Nigmatullin, V. R., & Rudnev, N. A. (2019) · Oil and Gas Business · doi:10.17122/ogbus-2019-5-202-238 · Cited by 1

Few-Shot Learning on 3D Surface Defect Detection with PM Networks

Chen, G., Yu, H., Jiang, L., & Shang, H. (2021) · International Conference on Artificial Intelligence and Advanced Manufacture (AIAM) · doi:10.1145/3495018.3495037 · Cited by 1

Predictive state analytics of gas pumping units

Краснов, А. Н., & Prakhova, M. Y. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2388/1/012078 · Cited by 1

Defect Recognition of Metal Components Based On Transfer Learning and Feature Fusion

Li, Y., Junren, Z., Hailong, L., & Chen, X. (2022) · International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) · doi:10.1109/iccwamtip56608.2022.10016502 · Cited by 1

Saury Sensing for Packaging

Zou, M., You, M., Kageyama, Y., & Akashi, T. (2023) · IEEJ Transactions on Electrical and Electronic Engineering · doi:10.1002/tee.23776 · Cited by 1

Steel Defect Detection Based on Attention Mechanism

Li, W., Yao, X., Yang, W., & Li, Y. (2023) · IEEE International Conference on Electronic Measurement & Instruments · doi:10.1109/icemi59194.2023.10269960 · Cited by 1

Research on surface defect detection of aerospace electronic components based on machine vision

Zhang, W., Pei, Z., Liu, K., & Lu, J. (2023) · International Conference on Optics and Communication Technology (ICOCT) · doi:10.1117/12.3017535 · Cited by 1

Identifying Quality Factors for Surface Defects on Small Steel Bars Using a Two-step Method of Statistical Difference Testing and k-means Clustering

Jeong, K.-H., Lee, D., Lee, S., Lim, S.-J., & Moon, H.-K. (2023) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-023-00941-1 · Cited by 1

Deep U-NET Based Heating Film Defect Inspection System

Hwang, J. W., Park, H. J., & Yi, H. (2024) · International Journal of Precision Engineering and Manufacturing · doi:10.1007/s12541-023-00937-x · Cited by 1

An Automotive Industry Vision Inspection System using Big Data Analytic System

Kwon, D., Lee, J.-S., & Yoo, N.-H. (2019) · Proceedings of the Korean Institute of Information and Commucation Sciences Conference · Cited by 1

Data Analytical Framework for Internet of Things

Saleem, T. J., Zahra, S. R., & Chishti, M. A. (2020) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa51403.2020.9317194 · Cited by 1

Threshold Image Segmentation for Non-uniform Illumination Using Otsu Optimization Approach

Liang, Q., Zicheng, Z., Hu, J., & Chang, K. (2021) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-69717-4_93 · Cited by 1

A Technology of Surface Defects for the Solar Wafer by Dual Vision

Huang, Y., Yi, M., Yang, W., & Yang, M. (2021) · IOP Conference Series Earth and Environmental Science · doi:10.1088/1755-1315/769/4/042007 · Cited by 1

Optical inspection of appearance faults for auto mirrors using Fourier filtering and convex hull arithmetic

Chiu, Y.-S. P., Lin, H.-D., & Cheng, H.-H. (2021) · Journal of Applied Research and Technology · doi:10.22201/icat.24486736e.2021.19.4.958 · Cited by 1

Human-Machine Collaborative Classification Model for Industrial Product Defect

Liu, Y., Wang, K., & Cheng, X. (2021) · International Conference on Computational Intelligence and Security (CIS) · doi:10.1109/cis54983.2021.00038 · Cited by 1

Piezoresistor defect classification using convolutional neural networks based on incremental branch growth

Huang, L., Yi-gong, Z., & Yang, T. (2022) · Multimedia Tools and Applications · doi:10.1007/s11042-022-12651-3 · Cited by 1

How to Face Unseen Defects? UDGAN for Improving Unseen Defects Recognition

Chen, Y., Hu, R., Wang, Z., & Li, Y. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-15919-0_16 · Cited by 1

One-stage defect detection method for plastic caps

Hong, X., Zhao, W., Huang, J., Zou, H., & Chen, Y. (2023) · International Conference on Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI) · doi:10.1117/12.3011997 · Cited by 1

Deep Learning Architectures for IoT Data Analytics

Mushtaq, S., & Singh, O. (2021) · Wiley eBooks · doi:10.1002/9781119740780.ch5 · Cited by 1

Image Classification With Convolutional Neural Networks In MapReducePreprint

Chen, M. (2021) · Research Square · doi:10.21203/rs.3.rs-1131730/v1 · Cited by 1

Vision Guidance System for Component Handling Robot

Mqaqa, S. V., Vermaak, H. J., & Luwes, N. J. (2019) · International Conference on Artificial Intelligence, Big Data, Computing and Data Communication Systems (icABCD) · doi:10.1109/icabcd.2019.8851028

Neighborhood preserving perceptual fidelity aware MSE for visual inspection of industrial flat surface products

Ameen, M., Alrahmawy, M. F., Aboueleneen, A., & Tolba, A. (2020) · Journal of Intelligent & Fuzzy Systems · doi:10.3233/jifs-192071

Automated inspection of contour faults for convex mirrors using wavelet descriptors and EWMA control scheme

林宏達, Lin, Y.-K., & Lin, H.-D. (2020) · International journal of innovative computing, information & control

SEMICONDUCTOR DEFECT CLASSIFICATION

Sweeney, T., Kerr, D., & Coleman, S. (2020) · Ulster University Research Portal (Ulster University)

Implementation of machine learning to increase productivity in the manufacturing industry: a literature review.

Purmala, Y. A. (2021) · Operations Excellence Journal of Applied Industrial Engineering · doi:10.22441/oe.2021.v13.i2.026 · Cited by 3

Computer-Aided Industrial Inspection of Vehicle Mirrors Using Computer Vision Technologies

Lin, H.-D., & Cheng, H.-H. (2021) · Transactions on computational science and computational intelligence · doi:10.1007/978-3-030-71051-4_20

Image Classification Applied to the Problem of Conformity Check in Industry

Mokhtari, N. I., Jovančević, I., Abdallah, H. B., & Orteu, J. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-18050-7_33

Detection of product container defect using automatic visual inspection: System design and performance evaluation

Ibrahim, M. H., Maghfiroh, H., Ramelan, A., Pramono, S., & Rosyidi, C. N. (2023) · AIP conference proceedings · doi:10.1063/5.0115113

Defect Classification in Electronic Components Using Dual-Channel Multi-Modal Convolutional Network

程, 克. (2023) · Hans Journal of Data Mining · doi:10.12677/hjdm.2023.133027

Wafer Contour Curve Linearization Algorithm Research Based on Image for Detecting Minute Edge DefectsPreprint

Zhao, J., Teng, X., Liu, M., & Shen, B. (2023) · Research Square · doi:10.21203/rs.3.rs-3233335/v1

Improved hardware acceleration of image quality simulation for optical imaging system

Ji, L., Rongbiao, X., & Fanyi, Z. (2023) · International Conference on Computers, Information Processing and Advanced Education (CIPAE) · doi:10.1109/cipae60493.2023.00123

A Surface Defect Detection Method Based on Attention Mechanism for Workpieces

Qiu, J., Shen, Y., Feng, W., Zhang, H., Sun, K., & Qin, Y. (2023) · IEEE International Conference on Cognitive Computing and Complex Data (ICCD) · doi:10.1109/iccd59681.2023.10420733

Surface Defect Detection Method Based on Cross-Data Space Adversarial Defense

Shi, J., Zhang, Z., Peng, X., Zhou, D. X., Zhao, X., & Liu, H. (2023) · China Automation Congress (CAC) · doi:10.1109/cac59555.2023.10450465

Qualification of AI-Based Surface Topography Inspection Systems for Inline Measurement in Series Production: Tactile Touch Systems Versus Optical AI Analysis

Hinz, M., Lindworsky, A., & Bracke, S. (2024) · Lecture notes in mechanical engineering · doi:10.1007/978-3-031-56467-3_12

Strategic Action Line LI6: Evolving and Resilient Production

Secchi, C., Golinelli, F., Semeraro, L., Benatti, P., Gilioli, E., Macchi, M., Bosi, G., Salmoiraghi, S., & Tomesani, L. (2024) · Springer tracts in mechanical engineering · doi:10.1007/978-3-031-60560-4_10

Development and Testing of a Wood Panels Bark Removal Equipment Based on Deep LearningPreprint

Wang, R., Zhang, G., Chen, H., Yu, X., Chen, Y., Liang, F., Mou, X., & Wang, B. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.5061380

Unveiling the Potential of Combined CNN-RF Fusion Strategies for Accurate Fabric Flaw Forecasting

Banerjee, D., Sharma, N., Upadhyay, D., & Dangi, S. (2024) · Asian Conference on Innovation in Technology (ASIANCON) · doi:10.1109/asiancon62057.2024.10838033

Deep Learning-Based Detection of Coating Sagging Defects

Jia, Q., Lu, D., Tian, X., & Wang, G. (2025) · Lecture notes in computer science · doi:10.1007/978-981-96-0777-8_20

Deep Learning-Based Method for Detecting Surface Defects in Air Filters

Yuan, Y., Lin, Y., & Zhang, T. (2024) · International Conference on Computer, Vision and Intelligent Technology (ICCVIT) · doi:10.1109/iccvit63928.2024.10872413

A nondestructive detection method for Si 3 N 4 bearing roller microcrack

Wu, N., Liao, S., Dong, W., Jiang, H., Wang, X., & Li, J. (2025) · International Journal of Applied Ceramic Technology · doi:10.1111/ijac.15082

Detection of Surface Defects of Barrel Media Based on PaE-VGG Model

Peng, H., Cheng, L., & Tian, J. (2025) · Mathematics · doi:10.3390/math13071104

Deep learning-based classification of lumbar T2-weighted MRIs to subjects with and without low back pain symptomsPreprint

Al-Rubaye, M., Inkinen, S. I., Karppinen, J. I., Nieminen, M. T., & Ketola, J. H. J. (2025) · Research Square · doi:10.21203/rs.3.rs-6367177/v1

Detecting Algorithm based on the Improved YOLOv8s for a Weak Feature Defect of Aviation Clamps

Luo, Q., & Liao, Y. (2025) · International Journal of Mechanical and Electrical Engineering · doi:10.62051/ijmee.v4n3.03

Papers Steel Defect Detection Model Based on State Space Model

Lu, Y., & Li, R. (2025) · International Conference on Neural Networks, Information and Communication Engineering (NNICE) · doi:10.1109/nnice64954.2025.11063822

Design and Optimization of Welding Defect Detection on Assembly Line Using Machine Vision System

Böğrekçi, İ., Demircioğlu, P., Benzer, E., Uslu, R., Demiroz, K., & Ekici, H. (2025) · International Symposium on Innovative Approaches in Smart Technologies (ISAS) · doi:10.1109/isas66241.2025.11101723

Cost Effective Machine Vision System for RealTime Defect Detection and Energy Efficiency in Learning Factories

Deepa, D., Yoganand, S., Balaji, N., & Bhattacharjee, K. (2025) · IEEE International Conference on Emerging Technologies and Applications (MPSec ICETA) · doi:10.1109/mpseciceta64837.2025.11118452

An automated approach for prostate cancer detection using CGAN data augmentation with a weighted ensemble of transfer learning models

Munshi, R. M. (2025) · International Journal of ADVANCED AND APPLIED SCIENCES · doi:10.21833/ijaas.2025.12.008

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Research on High Precision Positioning Platform Based on Real Time Ethernet

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Deep-Learning-Based Computer Vision System for Surface-Defect Detection

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Surface Defect Detection for Automated Inspection Systems using Convolutional Neural Networks

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Machine Vision Detection Method for Surface Defects of Automobile Stamping Parts

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A smart surface inspection system using faster R-CNN in cloud-edge computing environment

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