Railway track fastener defect detection based on image processing and deep learning techniques: A comparative study

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 114,148

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155

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

Ren, S., He, K., Girshick, R., & Sun, J. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2016.2577031 · Cited by 73,472

ImageNet: A large-scale hierarchical image database

Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333

Distinctive Image Features from Scale-Invariant Keypoints

Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562

A Threshold Selection Method from Gray-Level Histograms

Otsu, N. (1979) · IEEE Transactions on Systems, Man and Cybernetics · doi:10.1109/tsmc.1979.4310076 · Cited by 44,708

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Girshick, R., Donahue, J., Darrell, T., & Malik, J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.81 · Cited by 32,684

Latent dirichlet allocation

(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337

Fast R-CNN

Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105

A Railway Similarity Multiple Object Tracking Framework Based on Vehicle Front Video

Lian, L., Qin, Y., Cao, Z., Gao, Y., Bai, J., Ge, X., & Yu, H. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-99-9319-2_9

Investigation of the UAV Capability for Measuring Building's Cracks Dimensions

Abdulrahman, F. H., & Adam, S. B. (2024) · passer · doi:10.24271/psr.2024.188472

Semi-Automated Visual Quality Control Inspection During Construction or Renovation of Railways Using Deep Learning Techniques and Augmented Reality Visualization

Gounaridou, A., Pantraki, E., Dimitriadis, V., Tsakiris, A., Ioannidis, D., & Tzovaras, D. K. (2023) · Proceedings e report · doi:10.36253/10.36253/979-12-215-0289-3.86

Structure Health Diagnosis of Metro Rail Track by Using Vibration Mappings and Machine Learning

Saxena, M., Jain, P., Dhiman, P., & Singh, P. (2024) · Lecture notes in mechanical engineering · doi:10.1007/978-981-99-5922-8_3

Detection and Classification of Railway Track Surface Erosion-caused Holes and Scratches Defects Based on YOLOv5s

Yang, C., Dimyati, K., & Mohamad, M. (2024) · Tehnicki vjesnik - Technical Gazette · doi:10.17559/tv-20240422001485

Semi-supervised Anomaly Detection for Real-World Railway Surveillance via Restraining the Overgeneralization

Zhao, B., Li, P., Xue, R., & Dai, M. (2024) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-3682-9_49

Abnormal cable detection at the bottom of High-speed Train Using Improved Yolov8

Xu, X., & Lu, X. (2024) · International Conference on Video, Signal and Image Processing (VSIP) · doi:10.1145/3708568.3708572

Esd-Induced Display Anomaly Detection Using Template Difference Autoencoder ClusteringPreprint

Hou, L., Zeng, F., Wang, S., Wu, Y., & Bu, X. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5160301

Dynamic analysis of railroad track faults based on neural network algorithms

Lyu, Y., Sun, J., Cao, Y., Sun, J., Tian, M., Wang, H., & Wu, F. (2025) · Applied Mathematics and Nonlinear Sciences · doi:10.2478/amns-2025-0300

AI-Powered Monitoring for Mitigating Human-Animal Conflicts in Agricultural and Forest Zones

Murugadoss, R. (2025) · International Conference on Data Science, Agents & Artificial Intelligence (ICDSAAI) · doi:10.1109/icdsaai65575.2025.11011721

Performance optimization of rail inspection robot system based on deep vision and machine learning

Shen, H., Lan, L., Zhou, L., & Wang, H. (2025) · Journal of Computational Methods in Sciences and Engineering · doi:10.1177/14727978251348628

Railway Fastener Pixel-Level Detection Based on Dual-Stream Encoder Network With Mask Guidance

Qiu, Y., Liu, H., Ni, X., Shi, B., Jie, H., & Liu, J. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3578438

Rail Defect Measurement System: Integrating AI and IoT for Predictive Operations

Kumar, S. (2021) · International Journal of Artificial Intelligence Data Science and Machine Learning · doi:10.63282/3050-9262.ijaidsml-v2i2p105

Computational techniques

Zaheer, Q., Qiu, S., Wang, W., Shah, S. M. A. H., Yamin, S., Zhang, Y., Xiao, C., Zhao, D., Wajid, S., & Zhao, Y. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-34017-8.00006-5

Fundamentals of computer science for infrastructure management

Wang, H., Zaheer, Q., Qiu, S., Malik, M., Peng, J., Naeem, M., Ehsan, H., & Shah, S. M. A. H. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-34017-8.00002-8

Revolutionizing railway maintenance: the advent of inspection robots

Jing, G., & Qin, X. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33368-2.00019-3

Artificial Intelligence-Based Smart Automatic Railway Crack Detection and Protection

Daivagna, A. N., Kumar, S., Salimath, K., Al-Qawasmi, K. E., Purushotham, D., Kumar, D. S., Daga, I., & Giri, J. P. (2025) · Studies in systems, decision and control · doi:10.1007/978-3-031-89771-9_32

Visual Measurement and Uncertainty Prediction of Insulator Thickness in Insulated Rail Joints

Kasai, R., & Okatani, T. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3633568

Two-Stage Multi-Label Detection Method for Railway Fasteners Based on Type-Guided Expert Model

Lv, D., Meng, J., Meng, G., Shen, Y., Yao, L., & Liu, G. (2025) · Applied Sciences · doi:10.3390/app152413093

Research on Few-Shot Defect Detection Integrating Hybrid Attention Mechanism and Siamese Network

Bi, Y., Li, Q., & Wang, Y. (2025) · International Conference on Artificial Intelligence and Automation Control (AIAC) · doi:10.1109/aiac68175.2025.11332471

Two-stage positioning optimization method for ballasted railway track fastener

Su, F., Li, Y., Wang, P., Cai, W., Zhang, T., Cui, M., Wu, H., & Li, Z. (2026) · Engineering Research Express · doi:10.1088/2631-8695/ae3ba9

MSI-YOLO: An Improved YOLOv8n Algorithm for PCB Surface Defect Detection

Li, Y., & Wen, T. (2025) · International Conference on Electronic Information Engineering and Computer Technology (EIECT) · doi:10.1109/eiect68017.2025.11331785

Design and Production of a Prototype Conveyor Belt for Real-Time Surface Imaging and Data Set Creation

Özcan, T., Baktır, T., Toprak, A. N., Şahin, Ö., Yildiz, Ö. E., Yalçın, K., & Korkmaz, F. (2026) · Deu Muhendislik Fakultesi Fen ve Muhendislik · doi:10.21205/deufmd.2026288219

Inferential measurement method for track structure service status of high-speed railway based on CWT-ViT

Jiang, H., Xu, F., Zhao, Y., Zheng, X., Duan, X., & Zhou, L. (2026) · Measurement · doi:10.1016/j.measurement.2026.120914

Onboard Instance Segmentation of Railway Infrastructure and Obstacles for Autonomous Train Systems

Fedorov, V. A. (2025) · Doklady Mathematics · doi:10.1134/s1064562425700413

Single-domain generalization for fastener detection via sample reconstruction and class-wise domain contrast

Su, S., Du, S., Wei, X., & Lu, X. (2026) · Pattern Recognition · doi:10.1016/j.patcog.2026.113620

RailNet: Railway Track Anomaly Detection via Image Processing With Hybrid Deep Learning Techniques

Saeed, U., Waqas, M., Shah, A. A., Laghari, A. A., Almadhor, A. S., Kryvinska, N., Rajab, R., Laghari, R. A., & Kausar, S. (2026) · IET Image Processing · doi:10.1049/ipr2.70361

Six-DoF motion tracking of railway ballast particles using machine vision

Xue, M., Pang, T., Liu, K., Chen, R., Lü, T., & Wang, P. (2026) · Automation in Construction · doi:10.1016/j.autcon.2026.106969

CNN-LSTM-Based Approach for Fault Localization Using a Single Acoustic Emission Sensor in Railway Health MonitoringPreprint

Dey, S., & Datta, A. K. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6615890

HPCL: Hierarchical prototype-aware contrastive learning for open-world fastener defect detection

(286629), X. W., Xing, W., Zhang, G. F., Liu, W., Zhao, Z., & Zhang, C. (2026) · Measurement Science and Technology · doi:10.1088/1361-6501/ae65c7

AI-assisted fibre optic sensing frameworks for automated monitoring of railway infrastructure

Siahkouhi, M., Rashidi, M., Kang, W., & Bozorg, B. (2026) · Journal of Infrastructure Intelligence and Resilience · doi:10.1016/j.iintel.2026.100214

HRL-Det: Hierarchical Reinforcement Learning for Sequential Object Detection in Aerial Imagery

Li, M., & Hu, Y. (2026) · Sensors · doi:10.3390/s26134232

Rail image harmonization dataset: a seed to generate evaluation resources for track vision inspection systems

He, Y., Zhao, Z., Han, Z., Chen, C., Hao, J., Fu, Q., Tao, S., & Wu, J. (2026) · Multimedia Systems · doi:10.1007/s00530-026-02451-2

Faster R-CNN-based surface defect detection for sheet metal inspection in intelligent manufacturing

Chen, C.-H., Maurya, S. N., Li, K.-Y., & Chang, H.-C. (2026) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-026-18383-0

Vision-based ballast particle motion monitoring using semantic and instance segmentation

Xue, M., Pang, T., Wang, R., Chen, R., Liu, K., Lü, T., & Wang, P. (2026) · Computer-Aided Civil and Infrastructure Engineering · doi:10.1016/j.cacaie.2026.100177

Hierarchical Visual Detection and Spatial-Temporal Grid Clustering for Dense LED Frequency Tracking in Railway Inspection Robots

Luo, R., Zang, Y., Liu, Y., & Liang, Z. (2026) · International Symposium on Control Engineering and Robotics (ISCER) · doi:10.1109/iscer70245.2026.11681704

Review the Application of Artificial Intelligence in Damage Detection of Urban Railway Tracks

Dinh, T. P., Bui, H. A., Nguyen, H. P. K., Nguyen, A. T. L., & Yonghan, A. (2026) · Lecture notes in civil engineering · doi:10.1007/978-981-95-6770-6_53

Hybrid Deep Learning Framework for Crack Detection and Condition Classification of Prestressed Concrete Sleepers

Tanimki, S. S. H., Ricci, S., & Tanimki, C. S. R. (2026) · CivilEng · doi:10.3390/civileng7040069

Research on Automatic Detection Method of Railway Fastener Defects Based on Image Processing

Qiu, Y., & Lv, Z. (2019) · Science Discovery · doi:10.11648/j.sd.20190706.19

Rail fastener defect inspection method for multi railways based on machine vision

Liu, J., Huang, Y., Wang, S., Zhao, X., Zou, Q., & Zhang, X. (2022) · Railway Sciences · doi:10.1108/rs-04-2022-0012 · Cited by 8

Railway fastener defect detection based on deep convolutional networks

Zhou, Y. X., Li, X., & Chen, H. (2020) · International Conference on Graphics and Image Processing (ICGIP) · doi:10.1117/12.2557231 · Cited by 8

Research on defect detection method of rail fasteners based on deep learning

Liu, H., Cao, Z., & Yang, M. (2025) · International Conference on Electromechanical Control Technology and Transportation (ICECTT) · doi:10.1117/12.3078595

A Deep Learning-Based Hybrid Approach to Detect Fastener Defects in Real-Time

Aydın, İ., Sevi, M., Akın, E., Güçlü, E., Karaköse, M., & Aldarwich, H. (2023) · Tehnicki vjesnik - Technical Gazette · doi:10.17559/tv-20221020152721 · Cited by 4