The DOI matches its Crossref record; 46 of the 49 references it cites were found in a registry or an index.
Railway track fastener defect detection based on image processing and deep learning techniques: A comparative study
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“However, the above deep learning-based industrial defect detection methods do not specifically optimize the model's ability to extract and fuse features, which is crucial for defect detection.”
From A deep learning model for steel surface defect detection (2024) · cites this paper as [19]
Citations
Cites
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
(2003) · Journal of Machine Learning Research · doi:10.5555/944919.944937 · Cited by 32,337
Girshick, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.169 · Cited by 28,105
Flagged first, then by citations
3 of the 49 references couldn’t be matched to a known paper.
Research works printed but not linked: 3
- Berry, A., Nejikovsky, B., Gilbert, X., Tajaddini, A., 2008. High speed video inspection of joint bars using advanced image collection and processing techniques. In: Proc. of World Congress on Railway Research, vol. 290. pp. 619–622.
- Jiajia, 2016, Research on Automatic Inspection Algorithm for Railway Fastener Defects Based on Computer Vision, J. China Railway Soc., 38, 73
- Zuwen, 2007, Overall Comments on Track Technology of High- speed Railway, J. Railway Eng. Soc., 41
Cited by
Sorted by citations
Yang, J., Li, S., Wang, Z., Dong, H., Wang, J., & Tang, S. (2020) · Materials · doi:10.3390/ma13245755 · Cited by 539
A literature review of Artificial Intelligence applications in railway systems
Tang, R., De Donato, L., Bes̆inović, N., Flammini, F., Goverde, R. M. P., Lin, Z., Liu, R., Tang, T., Vittorini, V., & Wang, Z. (2022) · Transportation Research Part C: Emerging Technologies · doi:10.1016/j.trc.2022.103679 · Cited by 324
Deep Learning and Machine Vision Approaches for Posture Detection of Individual Pigs
Nasirahmadi, A., Sturm, B., Edwards, S. A., Jeppsson, K.-H., Olsson, A.-C., Müller, S., & Hensel, O. (2019) · Sensors · doi:10.3390/s19173738 · Cited by 150
Wei, X., Wei, D., Suo, D., Jia, L., & Li, Y. (2020) · IEEE Access · doi:10.1109/access.2020.2984264 · Cited by 136
Self-citationA survey of real-time surface defect inspection methods based on deep learning
Liu, Y., Zhang, C., & Dong, X. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10475-7 · Cited by 136
Self-citationArtificial Intelligence in Railway Transport: Taxonomy, Regulations, and Applications
Bešinović, N., Donato, L. D., Flammini, F., Goverde, R. M. P., Lin, Z., Liu, R., Marrone, S., Nardone, R., Tang, T., & Vittorini, V. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3131637 · Cited by 127
A deep learning model for steel surface defect detection
Li, Z., Wei, X., Hassaballah, M., Li, Y., & Jiang, X. (2024) · Complex & Intelligent Systems · doi:10.1007/s40747-023-01180-7 · Cited by 122
Self-citationAn optimized railway fastener detection method based on modified Faster R-CNN
Bai, T., Yang, J., Xu, G., & Yao, D. (2021) · Measurement · doi:10.1016/j.measurement.2021.109742 · Cited by 116
Developments, challenges, and perspectives of railway inspection robots
Jing, G., Qin, X., Wang, H., & Deng, C. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104242 · Cited by 116
Kumar, A., Zhou, Y., Gandhi, C. P., Kumar, R., & Xiang, J. (2020) · Alexandria Engineering Journal · doi:10.1016/j.aej.2020.03.034 · Cited by 107
One class based feature learning approach for defect detection using deep autoencoders
Mujeeb, A., Dai, W., Erdt, M., & Sourin, A. (2019) · Advanced Engineering Informatics · doi:10.1016/j.aei.2019.100933 · Cited by 103
Defect classification based on deep features for railway tracks in sustainable transportation
Aydın, İ., Akın, E., & Karaköse, M. (2021) · Applied Soft Computing · doi:10.1016/j.asoc.2021.107706 · Cited by 96
Kumar, A., & Harsha, S. P. (2024) · International Journal of Transportation Science and Technology · doi:10.1016/j.ijtst.2024.06.006 · Cited by 111
A Defect Detection Method for Rail Surface and Fasteners Based on Deep Convolutional Neural Network
Zheng, D., Li, L., Zheng, S., Chai, X., Zhao, S., Tong, Q., Ji, W., & Guo, L. (2021) · Computational Intelligence and Neuroscience · doi:10.1155/2021/2565500 · Cited by 102
Arvin, R., Khattak, A. J., & Qi, H. (2020) · Accident Analysis & Prevention · doi:10.1016/j.aap.2020.105949 · Cited by 90
Singh, S. A., Kumar, A. S., & Desai, K. A. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.119623 · Cited by 86
Wu, Y., Meng, F., Qin, Y., Qian, Y., Xu, F., & Jia, L. (2022) · Advanced Engineering Informatics · doi:10.1016/j.aei.2022.101819 · Cited by 85
Self-citationQiu, S., Cai, B., Wang, W., Wang, J., Zaheer, Q., Liu, X., Hu, W., & Peng, J. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105363 · Cited by 88
Dixit, M., Chaurasia, K., & Mishra, V. (2021) · Expert systems with applications · doi:10.1016/j.eswa.2021.115530 · Cited by 68
Qi, H., Xu, T., Wang, G., Cheng, Y., & Chen, C. (2020) · Computers in Industry · doi:10.1016/j.compind.2020.103303 · Cited by 81
A Deep Learning Approach Towards Railway Safety Risk Assessment
Alawad, H., Kaewunruen, S., & An, M. (2020) · IEEE Access · doi:10.1109/access.2020.2997946 · Cited by 80
Tacho-less sparse CNN to detect defects in rotor-bearing systems at varying speed
Kumar, A., Vashishtha, G., Gandhi, C. P., Tang, H., & Xiang, J. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104401 · Cited by 77
Deep Learning for Optical Sensor Applications: A Review
Al‐Ashwal, N. H., Soufy, K. A. M. A., Hamza, M. E., & Swillam, M. A. (2023) · Sensors · doi:10.3390/s23146486 · Cited by 71
Deep learning in hospitality and tourism: a research framework agenda for future research
Essien, A. E., & Chukwukelu, G. (2022) · International Journal of Contemporary Hospitality Management · doi:10.1108/ijchm-09-2021-1176 · Cited by 73
A Review on Rail Defect Detection Systems Based on Wireless Sensors
Zhao, Y., Liu, Z., Yi, D., Yu, X., Sha, X., Li, L., Sun, H., Zhan, Z., & Li, W. J. (2022) · Sensors · doi:10.3390/s22176409 · Cited by 60
A Review of Metal Surface Defect Detection Technologies in Industrial Applications
Qiao, Q., Hu, H., Ahmad, A. A., & Wang, K. (2025) · IEEE Access · doi:10.1109/access.2025.3544578 · Cited by 71
Research on real-time detection method of rail corrugation based on improved ShuffleNet V2
Yang, H., Liu, J., Mei, G., Yang, D., Deng, X., & Duan, C. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106825 · Cited by 59
Automated detection of defects with low semantic information in X-ray images based on deep learning
Du, W., Shen, H., Fu, J., Zhang, G., Shi, X., & He, Q. (2020) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-020-01566-1 · Cited by 58
UAV-Driven Structural Crack Detection and Location Determination Using Convolutional Neural Networks
Choi, D., Bell, W., Kim, D., & Kim, J. (2021) · Sensors · doi:10.3390/s21082650 · Cited by 57
Deep learning computer vision for robotic disassembly and servicing applications
Brogan, D. P., DiFilippo, N. M., & Jouaneh, M. K. (2021) · Array · doi:10.1016/j.array.2021.100094 · Cited by 57
An Investigation of Railway Fastener Detection Using Image Processing and Augmented Deep Learning
Chandran, P., Asber, J., Thiery, F., Odelius, J., & Rantatalo, M. (2021) · Sustainability · doi:10.3390/su132112051 · Cited by 52
Guo, Q., Zhang, X., Li, J., & Li, G. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104544 · Cited by 52
Automatic Railroad Track Components Inspection Using Hybrid Deep Learning Framework
Wu, Y., Chen, P., Qin, Y., Qian, Y., Xu, F., & Jia, L. (2023) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2023.3265636 · Cited by 54
Self-citationCascade Learning Embedded Vision Inspection of Rail Fastener by Using a Fault Detection IoT Vehicle
Liu, J., Liu, H., Chakraborty, C., Yu, K., Shao, X., & Ma, Z. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3126875 · Cited by 48
Detection and Severity Evaluation of Combined Rail Defects Using Deep Learning
Sresakoolchai, J., & Kaewunruen, S. (2021) · Vibration · doi:10.3390/vibration4020022 · Cited by 47
Meng, Y., Yang, J., Liu, S., Xu, W. K., Chen, G., Niu, Z., Wang, M., Deng, T., Qin, Y., Han, M., et al. (2022) · Nano Energy · doi:10.1016/j.nanoen.2022.107667 · Cited by 47
Vision-based monitoring of railway superstructure: A review
Aela, P., Cai, J., Jing, G., & Chi, H.-L. (2024) · Construction and Building Materials · doi:10.1016/j.conbuildmat.2024.137385 · Cited by 46
Structural damage identification by using physics-guided residual neural networks
Wang, R., Li, J., Li, L., An, S., Ezard, B., Li, Q., & Hao, H. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.118703 · Cited by 46
Real-Time Inspection System for Ballast Railway Fasteners Based on Point Cloud Deep Learning
Cui, H. L., Li, J., Hu, Q., & Mao, Q. (2019) · IEEE Access · doi:10.1109/access.2019.2961686 · Cited by 46
An Improved Faster R-CNN for High-Speed Railway Dropper Detection
Guo, Q., Liu, L., Xu, W., Gong, Y., Zhang, X., & Jing, W. (2020) · IEEE Access · doi:10.1109/access.2020.3000506 · Cited by 47
Missing Small Fastener Detection Using Deep Learning
Xiao, L., Wu, B., & Hu, Y. (2020) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2020.3023509 · Cited by 45
High Speed Railway Fastener Defect Detection by Using Improved YoLoX-Nano Model
Hu, J., Qiao, P., Lv, H., Yang, L., Ouyang, A., He, Y., & Liu, Y. (2022) · Sensors · doi:10.3390/s22218399 · Cited by 49
Self-citationTrack Fastener Defect Detection Model Based on Improved YOLOv5s
Li, X., Wang, Q., Yang, X., Wang, K., & Zhang, H. (2023) · Sensors · doi:10.3390/s23146457 · Cited by 47
A Survey on Audio-Video Based Defect Detection Through Deep Learning in Railway Maintenance
Donato, L. D., Flammini, F., Marrone, S., Mazzariello, C., Nardone, R., Sansone, C., & Vittorini, V. (2022) · IEEE Access · doi:10.1109/access.2022.3183102 · Cited by 48
Bianchi, G., Fanelli, C., Freddi, F., Giuliani, F., & Placa, A. L. (2025) · Advances in Mechanical Engineering · doi:10.1177/16878132241285631 · Cited by 61
Intelligent detection of fastener defects in ballastless tracks based on deep learning
Ye, W., Ren, J., Lu, C., Zhang, A. A., Zhan, Y., & Liu, J. (2024) · Automation in Construction · doi:10.1016/j.autcon.2024.105280 · Cited by 42
Railway Fastener Fault Diagnosis Based on Generative Adversarial Network and Residual Network Model
Yao, D., Sun, Q., Yang, J., Liu, H., & Zhang, J. (2020) · Shock and Vibration · doi:10.1155/2020/8823050 · Cited by 41
Liu, J., Ma, Z., Qiu, Y., Ni, X., Shi, B., & Liu, H. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3095167 · Cited by 41
Guo, G., Cui, X., & Du, B. (2021) · Applied Sciences · doi:10.3390/app11114756 · Cited by 41
Minguell, M. G., & Pandit, R. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.106622 · Cited by 42
Wang, S., Liu, F., & Liu, B. (2022) · Sensors · doi:10.3390/s22020413 · Cited by 40
Image quality enhancement using HybridGAN for automated railway track defect recognition
Cheng, M.-Y., Khasani, R. R., & Setiono, K. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104669 · Cited by 39
Dynamic prediction models of rock quality designation in tunneling projects
Mahmoodzadeh, A., Mohammadi, M., Ali, H. F. H., Abdulhamid, S. N., Ibrahim, H. H., & Noori, K. M.-G. (2020) · Transportation Geotechnics · doi:10.1016/j.trgeo.2020.100497 · Cited by 38
Liu, J., Teng, Y. T., Ni, X., & Liu, H. (2021) · IEEE Sensors Journal · doi:10.1109/jsen.2021.3062021 · Cited by 38
Zhan, Y. J., Dai, X., Yang, E., & Wang, K. C. P. (2020) · International Journal of Rail Transportation · doi:10.1080/23248378.2020.1825128 · Cited by 36
Wei, D., Wei, X., Tang, Q., Jia, L., Yin, X., & Ji, Y. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.105822 · Cited by 37
Self-citationWang, W., Yin, Q., Ai, C., Wang, J., Zaheer, Q., Niu, H., Cai, B., Qiu, S., & Peng, J. (2024) · Engineering Structures · doi:10.1016/j.engstruct.2024.119229 · Cited by 36
Track Defect Detection for High-Speed Maglev Trains via Deep Learning
He, Y., Wu, J., Zheng, Y., Zhang, Y., & Hong, X. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3151165 · Cited by 35
A Review of Computer Vision for Railways
Olivier, B., Guo, F. H., Qian, Y., & Connolly, D. P. (2025) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2025.3552011 · Cited by 34
Train-based differential eddy current sensor system for rail fastener detection
Chandran, P., Rantatalo, M., Odelius, J., Lind, H., & Famurewa, S. M. (2019) · Measurement Science and Technology · doi:10.1088/1361-6501/ab2b24 · Cited by 34
Munian, Y., Martínez-Molina, A., Miserlis, D., Hernández, H., & Alamaniotis, M. (2022) · Applied Artificial Intelligence · doi:10.1080/08839514.2022.2031825 · Cited by 39
A Review on Deep Learning Techniques for Railway Infrastructure Monitoring
di Summa, M., Griseta, M. E., Mosca, N., Patruno, C., Nitti, M., Renò, V., & Stella, E. (2023) · IEEE Access · doi:10.1109/access.2023.3309814 · Cited by 33
Chandran, P., Thiery, F., Odelius, J., Famurewa, S. M., Lind, H., & Rantatalo, M. (2021) · Applied Sciences · doi:10.3390/app11094018 · Cited by 33
Su, S., Ду, С., & Lu, X. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2022.3207490 · Cited by 33
Rail fastener detection of heavy railway based on deep learning
Cao, Y., Chen, Z., Wen, T., Roberts, C., Sun, Y., & Su, S. (2022) · High-speed Railway · doi:10.1016/j.hspr.2022.11.001 · Cited by 33
Decision intelligence-driven predictive modelling of air quality index in surface mining
Kamran, M., Jiskani, I. M., Wang, Z., & Zhou, W. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.108399 · Cited by 32
Ensemble model for rail surface defects detection
Li, H., Wang, F., Liu, J., Song, H., Hou, Z., & Dai, P. (2022) · PLoS ONE · doi:10.1371/journal.pone.0268518 · Cited by 31
A real-time railway fastener inspection method using the lightweight depth estimation network
Zhong, H., Liu, L., Wang, J., Fu, Q., & Yi, B. (2022) · Measurement · doi:10.1016/j.measurement.2021.110613 · Cited by 29
Railway rutting defects detection based on improved RT-DETR
Yu, C., & Chen, X. (2024) · Journal of Real-Time Image Processing · doi:10.1007/s11554-024-01530-9 · Cited by 28
Detection of Foreign Matter on High-Speed Train Underbody Based on Deep Learning
He, D., Yao, Z., Zhou, J., Chen, Y., Deng, J., & Xiang, W. (2019) · IEEE Access · doi:10.1109/access.2019.2960439 · Cited by 28
Han, S., Xiao, X., Song, B., Guan, T., Zhang, Y., & Lyu, M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107024 · Cited by 27
Counting trees with point-wise supervised segmentation network
Tong, P., Han, P., Li, S., Li, N., Bu, S., Li, Q., & Li, K. (2021) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2021.104172 · Cited by 28
Han, P., Ma, C., Jian, C., Chen, L., Bu, S., Xu, S., Zhao, Y., Zhang, C., & Hagino, T. (2022) · Remote Sensing · doi:10.3390/rs14164113 · Cited by 29
A hierarchical learning approach for railway fastener detection using imbalanced samples
Liu, J., Teng, Y. T., Shi, B., Ni, X., Xiao, W., Wang, C., & Liu, H. (2021) · Measurement · doi:10.1016/j.measurement.2021.110240 · Cited by 26
Application of machine vision-based NDT technology in ceramic surface defect detection – a review
Dong, G., Sun, S., Wang, Z., Wu, N., Huang, P., Feng, H., & Pan, M. (2022) · Materials Testing · doi:10.1515/mt-2021-2012 · Cited by 26
AnoDFDNet: A Deep Feature Difference Network for Anomaly Detection
Wang, Z., Zhang, Y., Luo, L., & Wang, N. (2022) · Journal of Sensors · doi:10.1155/2022/3538541 · Cited by 26
Eunus, S. I., Hossain, S., Ridwan, A. E. M., Adnan, A., Islam, M. S., Karim, D. Z., Alam, M. G. R., & Uddin, J. (2024) · AI · doi:10.3390/ai5020024 · Cited by 25
A Novel YOLOv5_ES based on lightweight small object detection head for PCB surface defect detection
橘高, 義., Li, Z., Wang, Y., & Zhu, S. (2024) · Scientific Reports · doi:10.1038/s41598-024-74368-7 · Cited by 29
The use of CNNs in VR/AR/MR/XR: a systematic literature review
Cortes, D., Bermejo, B. G., & Juiz, C. (2024) · Virtual Reality · doi:10.1007/s10055-024-01044-6 · Cited by 42
Automatic detection of surface defects based on deep random chains
Zhang, T., Wang, Z., Li, F., Zhong, H., Hu, X., Zhang, W., Zhang, D., & Liu, X. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.120472 · Cited by 25
Wang, W., Niu, H., Qiu, S., Wang, J., Luo, Y., Zaheer, Q., & Peng, J. (2024) · Journal of Computing in Civil Engineering · doi:10.1061/jccee5.cpeng-6026 · Cited by 22
Liu, T., Bao, J., Wang, J., & Wang, J. (2021) · International Journal of Computer Integrated Manufacturing · doi:10.1080/0951192x.2021.1901319 · Cited by 23
An improved SegNet network model for accurate detection and segmentation of car body welding slags
Zhu, D., Chen, Q., Qu, C., He, M., Zhang, S., Tu, Q., & Wei, W. (2022) · The International Journal of Advanced Manufacturing Technology · doi:10.1007/s00170-022-08836-7 · Cited by 22
Özdemir, R., & Koç, M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.124105 · Cited by 22
A multiple-stage defect detection model by convolutional neural network
Wang, K., Fan-Jiang, H., & Lee, Y.-X. (2022) · Computers & Industrial Engineering · doi:10.1016/j.cie.2022.108096 · Cited by 24
Kang, D., Lai, J., Zhu, J., & Yu, H. (2022) · Journal of Intelligent Manufacturing · doi:10.1007/s10845-022-02008-w · Cited by 21
ADNet: Anti-noise dual-branch network for road defect detection
Wan, B., Zhou, X., Sun, Y., Wang, T., Lv, C., Wang, S., Yin, H., & Yan, C. C. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2024.107963 · Cited by 21
Geomatic tools used in the management of agricultural activities: a systematic review
Escandón-Panchana, P., Herrera-Franco, G., Jaya-Montalvo, M., & Martínez-Cuevas, S. (2024) · Environment Development and Sustainability · doi:10.1007/s10668-024-04576-8 · Cited by 25
Qiu, S., Zaheer, Q., Shah, S. M. A. H., Shah, S. F. H., Ehsan, H., Atta, Z., Ai, C., Wang, J., Wang, W., & Peng, J. (2024) · Measurement · doi:10.1016/j.measurement.2024.116557 · Cited by 20
Chandran, P., Thiery, F., Odelius, J., Lind, H., & Rantatalo, M. (2022) · Sustainability · doi:10.3390/su14021035 · Cited by 20
Rail Fastener Status Detection Based on MobileNet-YOLOv4
Fu, J., Chen, X., & Lv, Z. (2022) · Electronics · doi:10.3390/electronics11223677 · Cited by 20
RFS-Net: Railway Track Fastener Segmentation Network With Shape Guidance
Su, S., Ду, С., Wei, X., & Lu, X. (2022) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2022.3212088 · Cited by 20
Self-citationÇelik, M. T., Arslankaya, S., & Yıldız, A. (2024) · Measurement · doi:10.1016/j.measurement.2024.114975 · Cited by 22
Long-Term Monitoring for Track Slab in High-Speed Rail via Vision Sensing
Li, Z.-W., He, Y.-L., Liu, X. Z., & Zhou, Y. (2020) · IEEE Access · doi:10.1109/access.2020.3017125 · Cited by 20
Haroon, M., Khan, M. J., Cheema, H. M., Nasir, M. T., Safdar, M., & Butt, S. I. (2024) · Results in Engineering · doi:10.1016/j.rineng.2024.103326 · Cited by 19
TriRNet: Real-Time Rail Recognition Network for UAV-Based Railway Inspection
Tong, L., Wang, Z., Jia, L., Qin, Y., Song, D., Miao, B., Tang, T. J., & Geng, Y. (2023) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2023.3328379 · Cited by 19
Self-citationCenter-Triplet Loss for Railway Defective Fastener Detection
Qiu, Y., Liu, H., Liu, J., & Shi, B. (2023) · IEEE Sensors Journal · doi:10.1109/jsen.2023.3339883 · Cited by 19
Industrial Printing Image Defect Detection Using Multi‐Edge Feature Fusion Algorithm
Liu, B., Chen, Y., Xie, J., & Chen, B. (2021) · Complexity · doi:10.1155/2021/2036466 · Cited by 19
Mask R-CNN Architecture Based Railway Fastener Fault Detection Approach
Yilmazer, M., & Karaköse, M. (2022) · International Conference on Decision Aid Sciences and Applications (DASA) · doi:10.1109/dasa54658.2022.9765024 · Cited by 18
Fault Detection and Semantic Segmentation on Railway Track Using Deep Fusion Model
Rampriya, R. S., Jenefa, A., Prathiba, S. B., Sabarinathan, Julus, L. J., Selvaraj, A. K., & Rodrigues, J. J. P. C. (2024) · IEEE Access · doi:10.1109/access.2024.3462822 · Cited by 21
Metadata
- Authors
- Wei, X., Yang, Z., Liu, Y., Wei, D., Jia, L., & Li, Y.
- Affiliations
- Beijing Jiaotong University (CN)
- Corresponding author
- Wei, Xiukun
- Venue
- Engineering Applications of Artificial Intelligence
- Publisher
- Elsevier BV
- Published
- 2019-04
- Volume
- 80
- Pages
- 66-81
- ISSN
- 0952-1976 (ISSN-L)
- OpenAlex
- W2912130719
- Cited by
- 338
- References
- 49
- Citations by year
- 2026: 25 · 2025: 48 · 2024: 66 · 2023: 48 · 2022: 65 · 2021: 48 · 2020: 26 · 2019: 12
- FWCI
- 28.24
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Engineering Applications of Artificial Intelligence (publishedVersion)
- Funders
- National Key R&D Program of China (2016YFB1200402)
- State Key Lab of Rail Traffic Control and Safety (RCS2019ZT0046)
- Topics
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- railway track fasteners, VGG16, Faster R-CNN, track safety
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review