Visual-Based Defect Detection and Classification Approaches for Industrial Applications—A SURVEY

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

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

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Multiresolution gray-scale and rotation invariant texture classification with local binary patterns

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Multilayer feedforward networks are universal approximators

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

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Smart In-Process Inspection in Human–Cyber–Physical Manufacturing Systems: A Research Proposal on Human–Automation Symbiosis and Its Prospects

Wang, S., & Jiao, J. R. (2024) · Machines · doi:10.3390/machines12120873 · Cited by 15

Improvement for Convolutional Neural Networks in Image Classification Using Long Skip Connection

Hoang, H. H., & Trinh, H. H. (2021) · Applied Sciences · doi:10.3390/app11052092 · Cited by 15

A survey for image based methods in construction: from images to digital twins

Koulalis, I., Dourvas, N. I., Triantafyllidis, T., Ioannidis, K., Vrochidis, S., & Kompatsiaris, I. Y. (2022) · International Workshop on Content-Based Multimedia Indexing (CBMI) · doi:10.1145/3549555.3549594 · Cited by 15

Automated fabric inspection through convolutional neural network: an approach

Thakur, R., Panghal, D., Jana, P., Rajan, & Prasad, A. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07891-1 · Cited by 14

GBC-BCD: an improved bridge crack detection method based on bidirectional Laplacian pyramid structure with lightweight attention mechanism convolution

Zhang, J., Chen, Z., Zou, H., Xue, S., He, J., & Li, J. (2024) · Nondestructive Testing And Evaluation · doi:10.1080/10589759.2024.2338921 · Cited by 13

Exploring and optimizing deep neural networks for precision defect detection system in injection molding process

Ghadoui, M. E., Mouchtachi, A., & Majdoul, R. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02394-3 · Cited by 13

Applications of Artificial Intelligence in Minimally Invasive Surgery Training: A Scoping Review

Caballero, D., Sánchez‐Margallo, J. A., Pérez-Salazar, M. J., & Sanchez-Margallo, F. M. (2025) · Surgeries · doi:10.3390/surgeries6010007 · Cited by 13

A Deep Transfer Learning-Based Visual Inspection System for Assembly Defects in Similar Types of Manual Tool Products

Lin, H.-D., Wu, H.-L., & Lin, C.-H. (2025) · Sensors · doi:10.3390/s25061645 · Cited by 13

Automated Optical Inspection Using Anomaly Detection and Unsupervised Defect Clustering

Lehr, J., Sargsyan, A., Pape, M., Philipps, J., & Krüger, J. (2020) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa46521.2020.9212172 · Cited by 14

Segmentation of Drilled Holes in Texture Wooden Furniture Panels Using Deep Neural Network

Augustauskas, R., Lipnickas, A., & Surgailis, T. (2021) · Sensors · doi:10.3390/s21113633 · Cited by 13

RAG-PaDiM: Residual Attention Guided PaDiM for Defects Segmentation in Railway Tracks

Kim, I., Jeon, Y., Kang, J. W., & Gwak, J. (2022) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-022-01346-2 · Cited by 15

Automatic fabric defect detection employing deep learning

Beljadid, A., Tannouche, A., & Balouki, A. (2022) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijece.v12i4.pp4129-4136 · Cited by 12

CBH-YOLO: A steel surface defect detection algorithm based on cross-stage Mamba enhancement and hierarchical semantic graph fusion

Gao, B., Tong, J., Fu, R., Zhang, Z., & Yuan, Y. (2025) · Neurocomputing · doi:10.1016/j.neucom.2025.131467 · Cited by 12

Zero-shot learning and classification of steel surface defects

Abdo, A. M. A. N., & Czúni, L. (2022) · International Conference on Machine Vision · doi:10.1117/12.2623570 · Cited by 12

The Steel Surface Multiple Defect Detection and Size Measurement System Based on Improved YOLOv5

Xu, Y., Ding, Z., Wang, L., Zhang, K., & Tong, L. (2023) · Journal of Electrical and Computer Engineering · doi:10.1155/2023/5399616 · Cited by 12

Intelligent IoT framework with GAN‐synthesized images for enhanced defect detection in manufacturing

Aramkul, S., & Sugunnasil, P. (2023) · Computational Intelligence · doi:10.1111/coin.12619 · Cited by 11

Micro LED defect detection with self-attention mechanism-based neural network

Zhong, Z., Li, C., Chen, M., Wu, H. Y., & Kiyoshi, T. (2024) · Digital Signal Processing · doi:10.1016/j.dsp.2024.104474 · Cited by 11

A Two-Stage YOLOv5s–U-Net Framework for Defect Localization and Segmentation in Overhead Transmission Lines

Li, A., Li, D., & Wang, A. (2025) · Sensors · doi:10.3390/s25092903 · Cited by 15

The detection of defects in ceramic cell phone backplane with embedded system

Huang, W., Zhang, C., Wu, X. L., Shen, J., & Li, Y. (2021) · Measurement · doi:10.1016/j.measurement.2021.109598 · Cited by 11

Defect Detection on LED Chips Based on Position Pre-Estimation and Feature Enhancement

Xu, L., Hu, X., He, T., Hu, K., & Zhang, J. (2022) · Applied Sciences · doi:10.3390/app12031265 · Cited by 11

Energy-efficient buildings with energy-efficient optimized models: a case study on thermal bridge detection

Fișne, A., Yurtsever, M. M. E., & Eken, S. (2024) · Cluster Computing · doi:10.1007/s10586-024-04624-y · Cited by 11

An Unsupervised Generative Adversarial Network-Based Method for Defect Inspection of Texture Surfaces

Wang, J., Yi, G., Zhang, S., & Wang, Y. (2020) · Applied Sciences · doi:10.3390/app11010283 · Cited by 10

Detecting faulty bottle caps using CNN model

Abhijit, M., & Priya, S. S. (2021) · International Conference on Smart Electronics and Communication (ICOSEC) · doi:10.1109/icosec51865.2021.9591780 · Cited by 10

Using deep learning to detect the presence/absence of defects on leather: on the way to build an industry-driven approach

Adão, T., Gonzalez, D. G., Castilla, Y. C., Pérez, J., Shahrabadi, S., Sousa, N., Guevara, M. A., & Magalhães, L. (2022) · Journal of Physics Conference Series · doi:10.1088/1742-6596/2224/1/012009 · Cited by 10

Spot Welding Parameter Tuning for Weld Defect Prevention in Automotive Production Lines: An ML-Based Approach

Bayır, M., Yücel, E., Kaya, T., & Yıldırım, N. (2023) · Information · doi:10.3390/info14010050 · Cited by 10

Fabric defect classification using transfer learning and deep learning

Beljadid, A., Tannouche, A., & Balouki, A. (2023) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v12.i3.pp1378-1385 · Cited by 10

Real-time defect detection of saw chains on automatic assembly lines based on residual networks and knowledge coding

Zhang, F., Wu, T., Liu, S., Zhu, Y., Chen, L., & Natsuki, T. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107507 · Cited by 10

Adaptive explainable artificial intelligence for visual defect inspection.

Rožanec, J. M., Šircelj, B., Fortuna, B., & Mladenić, D. (2024) · Procedia Computer Science · doi:10.1016/j.procs.2024.02.119 · Cited by 10

A Study on Sample Size Sensitivity of Factory Manufacturing Dataset for CNN-Based Defective Product Classification

Kim, D., Seo, S. B., Yoo, N., & Shin, G. (2022) · Computation · doi:10.3390/computation10080142 · Cited by 10

Image Data-Centric Visual Feature Selection on Roll-to-Roll Slot-Die Coating Systems for Edge Wave Coating Defect Detection

Lee, Y., Yun, J., Lee, S.-W., & Lee, C. (2024) · Polymers · doi:10.3390/polym16081156 · Cited by 9

A lightweight YOLOv11-based framework for small steel defect detection with a newly enhanced feature fusion module

Wang, Y., Sun, H., Luo, K., Zhu, Q., Li, H., Sun, Y., Wu, Z., & Wang, G. (2025) · Scientific Reports · doi:10.1038/s41598-025-16619-9 · Cited by 9

Machine learning in corrosion electrochemistry: A critical review of prediction and corrosion-type identification

Guo, R., & Rong, J. (2026) · International Journal of Electrochemical Science · doi:10.1016/j.ijoes.2026.101324 · Cited by 9

Review of the State-of-the-art Sewer Monitoring and Maintenance Systems Pune Municipal Corporation - A Case Study

Patil, R. R., Ansari, S. M., Calay, R. K., & Mustafa, M. Y. (2021) · TEM Journal · doi:10.18421/tem104-02 · Cited by 9

Encoding 3D Point Contexts for Self-Supervised Spall Classification Using 3D Bridge Point Clouds

Kasireddy, V., & Akinci, B. (2022) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-5041 · Cited by 9

A Real-Time Automated Visual Inspection System for Printed Circuit Boards Missing Footprints Detection

Cao, X. (2023) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2023.0140537 · Cited by 9

Automated optical inspection based on synthetic mechanisms combining deep learning and machine learning

Lo, C., & Lin, T. C. (2024) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-024-02474-4 · Cited by 9

Enhancing Defect Detection in Steel Plate Manufacturing with Explainable Machine Learning and SMOTE for Imbalanced Data

Dorbane, A., Harrou, F., & Sun, Y. (2025) · Journal of Materials Engineering and Performance · doi:10.1007/s11665-025-11136-2 · Cited by 9

AI Landing for Sheet Metal-Based Drawer Box Defect Detection Using Deep Learning (ALDB-DL)

Sheu, R., Chen, L.-C., Pardeshi, M. S., Pai, K.-C., & Chen, C.-Y. (2021) · Processes · doi:10.3390/pr9050768 · Cited by 9

72‐3: Deep Learning Based Visual Defect Detection in Noisy and Imbalanced Data

Cheng, Q., Qu, S., & Lee, J. (2022) · SID Symposium Digest of Technical Papers · doi:10.1002/sdtp.15658 · Cited by 9

Mobile-YOLO-SDD: A Lightweight YOLO for Real-time Steel Defect Detection

Luo, S., Xu, Y., Zhu, M., Zhang, C., Kong, C., Jin, J., Li, T., Jiang, X., & Guo, B. (2024) · Procedia CIRP · doi:10.1016/j.procir.2024.10.040 · Cited by 8

Sewer pipeline condition assessment and defect detection using computer vision

Nguyen, C. L., Nguyen, A., Brown, J., & Dang, L. M. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106479 · Cited by 9

Dual-path segmentation network for automatic fabric defect detection

Yu, Z., Xu, Y., Wang, Y., Sheng, X., & Xie, G. (2023) · Textile Research Journal · doi:10.1177/00405175231192057 · Cited by 8

Flexible Material Quality Assessment Based on Visual–Tactile Fusion

Xu, S., Xu, H., Mao, F., Su, W., Ji, M., Gan, H., & Yang, W. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3386205 · Cited by 8

Internal thread defect detection system based on multi-vision

Dou, X., Xue, C., Zhang, G., & Jiang, Z. (2024) · PLoS ONE · doi:10.1371/journal.pone.0304224 · Cited by 9

UTILIZING MACHINE LEARNING TO ASSESS DATA COLLECTION METHODS IN MANUFACTURING AND MECHANICAL ENGINEERING

Bappy, M. A., & Ahmed, M. (2024) · Academic journal on science, technology, engineering & mathematics education. · doi:10.69593/ajsteme.v4i02.73 · Cited by 8

Dual-Modal Illumination System for Defect Detection of Aircraft Glass Canopies

Li, Z., Yao, Y., Wen, R., & Liu, Q. (2024) · Sensors · doi:10.3390/s24206717 · Cited by 8

TGSYOLO: Template-Guidance Siamese Network for SMT Welding Defect Detection

Shi, K., Yu, C., Cao, Y. H., Kang, Y., Zhao, Y., Zhao, L., & Xu, Z. (2024) · IEEE Transactions on Components Packaging and Manufacturing Technology · doi:10.1109/tcpmt.2024.3491163 · Cited by 8

An efficient lightweight detection model for steel surface defects with dynamic deformable head

Li, C., Wen, Z., Huang, H., Mo, H., Zhou, S., & Zhu, Z. (2025) · Engineering Research Express · doi:10.1088/2631-8695/adbab4 · Cited by 8

Defects Detection in Highly Specular Surface using a Combination of Stereo and Laser Reconstruction

Dawda, A., & Nguyen, M. (2020) · International Conference on Image and Vision Computing New Zealand (IVCNZ) · doi:10.1109/ivcnz51579.2020.9290660 · Cited by 8

Imbalance Modelling for Defect Detection in Ceramic Substrate by Using Convolutional Neural Network

Huang, Y., Su, C.-M., Basanta, H., & Tsai, Y.-L. (2021) · Processes · doi:10.3390/pr9091678 · Cited by 8

An Image-Based Data-Driven Model for Texture Inspection of Ground Workpieces

Wang, Y.-H., Lai, J.-Y., Lo, Y.-C., Shih, C.-H., & Lin, P. (2022) · Sensors · doi:10.3390/s22145192 · Cited by 8

Development of an Intelligent Defect Detection System for Gummy Candy under Edge Computing

Chen, S.-W. C. S.-W., Chen, C.-J. T. S.-W., Tsai, C.-H. L. C.-J., Liu, W. C.-C. C. C.-H., & Chu, C.-T. T. W. C.-C. (2022) · 網際網路技術學刊 · doi:10.53106/160792642022092305006 · Cited by 8

Multimodal Natural Language Explanation Generation for Visual Question Answering Based on Multiple Reference Data

Zhu, H., Togo, R., Ogawa, T., & Haseyama, M. (2023) · Electronics · doi:10.3390/electronics12102183 · Cited by 8

Scalable and transparent automated sewer defect detection using weakly supervised object localization

Yin, J., Yin, X., Pan, M., & Long, L. (2025) · Automation in Construction · doi:10.1016/j.autcon.2025.106152 · Cited by 7

Багатоетапний метод глибинного навчання з попереднім самонавчанням для класифікаційного аналізу дефектів стічних труб

Москаленко, В. В., Зарецький, М. О., Москаленко, А. С., Коробов, А. Г., & Ковальський, Я. Ю. (2021) · RADIOELECTRONIC AND COMPUTER SYSTEMS · doi:10.32620/reks.2021.4.06 · Cited by 7

Micro-Scale Surface Recognition via Microscope System Based on Hu Moments Pattern and Micro Laser Line Projection

Rodríguez, J. A. M. (2023) · Metals · doi:10.3390/met13050889 · Cited by 7

EuroPallet Detection with RGB-D Camera Based on Deep Learning

Kırcı, S., & Atalı, G. (2023) · The European Journal of Research and Development · doi:10.56038/ejrnd.v3i4.341 · Cited by 7

Feature Enhancement Method for Magnetic Particle Testing Based on Isolation Strip

Li, Y., Kang, Y., Chen, Y., Guo, Y., Duan, Z., & Feng, B. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3373080 · Cited by 7

Infoecology of the deep learning and smart manufacturing: thematic and concept interactions

Asemi, A., Kő, A., & Asemi, A. (2021) · Library Hi Tech · doi:10.1108/lht-08-2021-0252 · Cited by 7

Automated vision based defect detection using gray level co-occurrence matrix for beverage manufacturing industry

Saad, N. M., Abdullah, A. R., Hasan, W. H. W., Rahman, N. N. S. A., Ali, N. H., & Abdullah, I. (2021) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v10.i4.pp818-829 · Cited by 7

Active inspection of objects to detect possible damage and measure their volume using 3D reconstruction

Riffo, V., & Hidalgo, R. (2022) · Measurement · doi:10.1016/j.measurement.2022.111541 · Cited by 7

Application of Computer Vision in Pipeline Inspection Robot

Oluwatosin, O. P., Syed, S. A., Apis, O., & Kolawole, S. (2021) · International Conference on Industrial Engineering and Operations Management (IEOM) · doi:10.46254/an11.20210374 · Cited by 7

Defects Localization in Images Using Deep Learning-Based Classification with CAM Output

Augustauskas, R., Zabulis, L., Lipnickas, A., & Jokubauskas, S. (2023) · IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS) · doi:10.1109/idaacs58523.2023.10348813 · Cited by 7

Localization and Force-Feedback with Soft Magnetic Stickers for Precise Robot Manipulation

Hellebrekers, T., Zhang, K., Veloso, M., Kroemer, O., & Majidi, C. (2020) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros45743.2020.9341281 · Cited by 7

Graph Neural Networks for patterned welds detection on point clouds

Welbeck, K., Zhang, Y., Yang, R., Li, G., Wang, H.-P., Carlson, B. E., & Guo, P. Y. (2025) · Journal of Manufacturing Processes · doi:10.1016/j.jmapro.2025.03.109 · Cited by 6

Inspection of mixed-type wafer defects from single-defect data via adaptive synthesis and self-attention CNN

Thuận, N. Đ. (2025) · Measurement · doi:10.1016/j.measurement.2025.118306 · Cited by 6

An approach for increasing the throughput of a CNN-based industrial quality inspections system with constrained devices

Hartner, R., Komar, J., & Mezhuyev, V. (2022) · International Conference on Software and Computer Applications (ICSCA) · doi:10.1145/3524304.3524330 · Cited by 6

Automated end-of-line quality assurance with visual inspection and convolutional neural networks

Kim, H., Frommknecht, A., Bieberstein, B., Stahl, J., & Huber, M. F. (2023) · tm - Technisches Messen · doi:10.1515/teme-2022-0092 · Cited by 6

Machine learning-based system to automate visual inspection in aerospace engine manufacturing

Rosell, A., Svenman, E., Westphal, P., Mukundan, A., Bhattacharya, S., Bharthulwar, S., Brahmachari, K. S., & Jhanardhanan, S. (2023) · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) · doi:10.1109/etfa54631.2023.10275515 · Cited by 6

Enhancing Battery Exterior Defect Inspection Accuracy Through Defect-Background Separated GAN Development

Ku, D., & Pahk, H.-J. (2024) · IEEE Access · doi:10.1109/access.2024.3380618 · Cited by 6

Middleware para Aplicações Distribuídas de Vídeo com Suporte à Computação na Borda na Indústria 4.0

Neto, O. D. A. R., Chaves, R. C., do Nascimento, A. P., & Gomes, R. D. (2024) · Brazilian Symposium on Multimedia and the Web · doi:10.5753/webmedia.2024.242927 · Cited by 6

PPLA-Transformer: An Efficient Transformer for Defect Detection with Linear Attention Based on Pyramid Pooling

Song, X., Tian, Y., Liu, H., Wang, L., & Niu, J. (2025) · Sensors · doi:10.3390/s25030828 · Cited by 6

A Novel Classification Method: Neighborhood-Based Positive Unlabeled Learning Using Decision Tree (NPULUD)

Ghasemkhani, B., Balbal, K. F., Birant, K. U., & Birant, D. (2024) · Entropy · doi:10.3390/e26050403 · Cited by 6

Surface Defect Inspection in Images Using Statistical Patches Fusion and Deeply Learned Features

Chian, Y. T. E., & Tian, J. (2021) · AI · doi:10.3390/ai2010002 · Cited by 6

Double Low-rank Based Matrix Decomposition for Surface Defect Segmentation of Steel Sheet

Zhou, S., Wu, S., Cui, K., & Liu, H. (2021) · ISIJ International · doi:10.2355/isijinternational.isijint-2021-024 · Cited by 6

A novel severity calibration algorithm for defect detection by constructing maps

Li, Y., Fan, B., Ding, W., Zhang, W., & Yin, J. (2022) · Information Sciences · doi:10.1016/j.ins.2022.06.076 · Cited by 6

A Robust Deep Learning Ensemble-Driven Model for Defect and Non-Defect Recognition and Classification Using a Weighted Averaging Sequence-Based Meta-Learning Ensembler

Stephen, O., Madanian, S., & Nguyen, M. (2022) · Sensors · doi:10.3390/s22249971 · Cited by 6

Chip Appearance Inspection Method for High-Precision SMT Equipment

Zhang, H., Sun, H., & Shi, P. (2021) · Machines · doi:10.3390/machines9020034 · Cited by 6

JCS: An Explainable Surface Defects Detection Method for Steel Sheet by Joint Classification and Segmentation

Zhou, S., Liu, H., Cui, K., & Hao, Z. (2021) · IEEE Access · doi:10.1109/access.2021.3117736 · Cited by 6

A Survey of Semi-supervised and Unsupervised Learning Methods for Industrial Defect Anomaly Detection

Zhang, J. (2025) · Journal of Computer Science and Frontier Technologies · doi:10.63313/jcsft.9001 · Cited by 5

Machine vision driven magnetic particle inspection technology: principles, applications and trends

Gao, Q. X., Wu, M., Qin, X., & Hua, L. (2025) · Measurement Science and Technology · doi:10.1088/1361-6501/ae2d81 · Cited by 5

Self-supervised learning for multi-label sewer defect classification

Yildizli, T., Jia, T., Langeveld, J., & Taormina, R. (2026) · Automation in Construction · doi:10.1016/j.autcon.2025.106751 · Cited by 5

Automated Quality Inspection Using Computer Vision: A Review

Belkhedar, G., & Lyhyaoui, A. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-26384-2_60 · Cited by 5

Metallic Mesh Defect Detection Based on Low-Rank Decomposition via Schatten Capped p Norm

Qin, H., Sun, M., Lu, Z., & Tan, J. (2024) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2024.3470039 · Cited by 5

Learning Simulation-Based Digital Twins for Discrete Material Flow Systems: A Review

Schwede, C., & Fischer, D. (2024) · Winter Simulation Conference · doi:10.1109/wsc63780.2024.10838729 · Cited by 5

PMSE-YOLO: an efficient framework for detecting surface defects of hot-rolled strip steel

Zhou, M., Wang, R., & Chen, Y. (2025) · Signal Image and Video Processing · doi:10.1007/s11760-025-04189-2 · Cited by 5

StripSurface-YOLO: An Enhanced Yolov8n-Based Framework for Detecting Surface Defects on Strip Steel in Industrial Environments

Li, H., Zhang, H., & Zang, W. (2025) · Electronics · doi:10.3390/electronics14152994 · Cited by 5

Improved U-Net++ with Patch Split for Micro-Defect Inspection in Silk Screen Printing

Yoon, B., Lee, H., & Jeong, J. (2022) · Applied Sciences · doi:10.3390/app12094679 · Cited by 5

Detection of printed circuit board faults with FPGA-based real-time image processing

Aydın, M., & Kaçar, F. (2023) · Iran Journal of Computer Science · doi:10.1007/s42044-023-00149-6 · Cited by 5

Non-destructive detection for mosaic ceramic surface defects based on convolutional neural networks

Dong, G., Sun, S., Kong, X., Wu, N., Zhang, H., Chen, X., Feng, H., Huang, P., & Wang, Z. (2023) · Materials Testing · doi:10.1515/mt-2023-0051 · Cited by 5

Body thickness and bottom defect detection methods for products made by bottle blowing machines to meet quality requirements

Tsai, M., Liu, B.-C., & Hsieh, S.-L. (2023) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-023-12693-3 · Cited by 5

Road Crack Detection using Yolo-V5 and Adaptive Thresholding

Suhendar, H. (2023) · Journal of Applied Intelligent System · doi:10.33633/jais.v8i3.9437 · Cited by 5

Effective multispike learning in a spiking neural network with a new temporal feedback backpropagation for breast cancer detection

Heidarian, M., Karimi, G., & Payandeh, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124010 · Cited by 5

Robust Anomaly Map Assisted Multiple Defect Detection with Supervised Classification Techniques

Rožanec, J. M., Zajec, P., Theodoropoulos, S., Koehorst, E., Fortunat, B., & Mladenić, D. (2023) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2023.10.1144 · Cited by 5

A System Deployment Model of Multi-CCD Automatic Optical Inspection for Economical Operations

Wang, K., & Qiu, Y.-C. (2022) · IEEE Access · doi:10.1109/access.2022.3178999 · Cited by 5

A Review of AIoT-based Edge Devices and Lightweight DeploymentPreprint

Sun, K., Wang, X., & Zhao, Q. (2023) · doi:10.36227/techrxiv.21687248.v2 · Cited by 5

A Practical Methodology for Accuracy and Quality Evaluation of Structured Light Systems in Automotive Inspection

Lagudi, A., Severino, U., Barbieri, L., & Bruno, F. (2025) · Machines · doi:10.3390/machines13070576 · Cited by 4

Simultaneous identification of time-varying suspension parameters and unknown road roughness in a nonlinear suspension system

Zhang, P., Sun, G., Hu, J., Yan, F., Chen, D., & Huang, Y. (2025) · Mechanical Systems and Signal Processing · doi:10.1016/j.ymssp.2025.113221 · Cited by 4

Application of Machine Learning Models for Classifying Wood Surface Defects Using Near-Infrared Spectroscopy

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Toward Automated Quality Control: An End-to-End Image Processing and Machine Learning Pipeline for Bullet Case Mouth Defect Detection

Ozkan-Okay, M. (2025) · IEEE Access · doi:10.1109/access.2025.3610358 · Cited by 4

A New Classification of Existing Techniques for Error/Defect Detection in Image Processing

Abdulzahra Saad Alsaide, H., Soltanaghaei, M. R., Zayer, W. H., & Asgarnezhad, R. (2024) · Majlesi Journal of Telecommunication Devices · doi:10.71822/mjtd.2024.1122151

Texture defect detection: a review

Song, K. Y., Petrou, M., & Kittler, J. (1992) · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · doi:10.1117/12.58564 · Cited by 22

Detection and classification of structural defects on textured surfaces

Schwarzer, H., & Teiwes, S. (1997) · Optics in Computing · doi:10.1364/oc.1997.jwa.5

Predictive Maintenance in Smart Manufacturing: A Deep Learning Approach for Equipment Anomaly Forecasting

Jiang, X. (2026) · MATEC Web of Conferences · doi:10.1051/matecconf/202642402013

Automated defect detection in textured materials

Pathak, A. K. (2001) · doi:10.5353/th_b3124222 · Cited by 17