A New Computer Vision Based Rail Detection Method Using Entropy and Support Vector Machines
DOI:
https://doi.org/10.24203/ijcit.v12i2.331Keywords:
classification, entropy, support vector machine, image processing, railwaysAbstract
Condition monitoring in railways is an important and critical process in terms of travel safety. However, this process is
generally done based on observation or with various equipment. Therefore, it is costly and has a high probability of error. In this
study, a computer vision-based method for rail detection for condition monitoring in railways is proposed. In addition to the
features obtained from the images, a new feature is calculated using entropy. Rail detection is provided by classifying these
features with Support Vector Machine (SVM). It has been seen that the proposed method works successfully and provides
improvement in the monitoring process.
References
https://uic.org/support-activities/statistics/
Jhilmill Mathur, U.S. Pandey, Facial Expression Recognition using Wavelet based Support Vector Machine, 2017 Recent Developments in Control, Automation & Power Engineering (RDCAPE), 2017.
S. Jasmine Minija, W.R. Sam Emmanuel, Food image Classification using Sphere Shaped-Support Vector Machine, 2017 Proceedings of the International Conference on Inventive Computing and Informatics (ICICI), 2017.
Shankaragowda B.B., Dr. Siddappa M., Dr. Suresha M., A Novel Approach for the Brain Tumor Detection and Classification using Support Vector Machine, 2017.
Yaman, O., Karakose, M., Complex Fuzzy Automata Based Fault Detection Approach Using thermal Images in Railways, 2018 International Conference on Artificial Intelligence and Data Processing (IDAP), 2018.
Yaman, O., Karakose, M., Akin, E., Aydin, I., Image Processing Based Fault Detection Approach for Rail Surface, 2015 23nd Signal Processing and Communications Applications Conference (SIU), 2015.
Tastimur, C., Yaman, O., Karakose, M., Akin, E., A Real Time Interface for Vision Inspection of Rail Components and Surface in Railways, 2017 International Artificial Intelligence and Data Processing Symposium (IDAP), 2017.
Karakose, M., Yaman, O., Complex Fuzzy System Based Predictive Maintenance Approach in Railways, IEEE Transactions on Industrial Informatics, 2020.
Akhilla, C., Diamond, C.,A., Posonia, A. M., Convolutional Neural Network Based Online Rail Surface Crack Detection, 5th International Conference on Intelligent Computing and Control Systems (ICICCS), 2021.
Zhang, Y., Deng, X., Huang, Y., Research on Railroad Turnout Fault Diagnosis Based on Support Vector Machine, 7th International Symposium on Mechatronics and Industrial Informatics (ISMII), 2021.
Saini, A., Agarwal, A., Singh, D., Feature-based Template Matching for Joggled Fishplate Detection in Railroad Track with Drone Images, IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2020.
Wang, Z., Cai, B., Chunxiao, J,. Tao, C., Zhang, Z., Wang, Y., Li, S., Huang, F., Fu, S., Zhang, F., Geomerty Constraints-based Visual rail track Extraction, 12th World Congress on Intelligent Control and Automation (WCICA), 2016.
Kaleli, F., Akgül, Y., S., Vision Based Railroad Track Extraction Using Dynamic Programming, 12th International IEEE Conference on Intelligent Transportation Systems, 2009.
Gschwandtner, M., Pree, W., Uhl, A., Track detection for autonomous trains, International Symposium on Visual Computing, 2010.
Milan Banic, Aleksandar Miltenovic, Milan Pavlovic, Ivan Ciric, Intelligent Machine Vision Based Railway Infrastructure Inspection and Monitoring Using UAV, Mechanical Engineering, 2019.
Bettemir, Ö., H., Detection of Railway Track from Image by Heurestic Method, 23nd Signal Processing and Communications Applications Conference (SIU), 2015.
Arun Kumar Singh, Anushka Swarup, Ankush Agarwall, Dharmendre Singh, Vision based rail track extraction and monitoring through drone imagery, The Korean Institute of Communications and Information Sciences, 2017.
AlNaimi, N., Qidwai, U., IoT Based on-the-fly Visual Defect Detection in Railway Tracks, IEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), 2020.
Ryndyk, A. G., Myakinkov, A. V., Balashova, D. M., Burov, V. N., Shabalin,, S. A., Mikhaylov, A. D., Railway safety radar system with use of FSR, The International Radar Symposium IRS, 2021.
Yanwei, J., Yu, D., Research on Railway Obstacle Detection Method Based on Radar, 7th International Symposium on Mechatronics and Industrial Informatics (ISMII), 2021.
Fan, H., Li, C., He, Z., Bai, Y., Huo, M., Xie, Y. J., Track quality detection method for groovedd rail, IEEE 6th International Conference on Intelligent Computing and Signal Processing (ICSP), 2021.
Piao, G., Li, J., Udpa, L., Udpa, S., Deng, Y., The Effetct of Motion-Induced Eddy Currents on Three-Axis MFL Signals for High-Speed Rail Inspection, IEEE Transactions on Magnetics, 2021.
Sliti, M., Boudriga, N., Building Structural Health Monitoring: an FBG-based estimation of external vibrations, 18th International Multi-Conference on systems, Signals & Devices (SSD), 2021.
Song, B., Lu, W., Xing, W., Wei, X., Yang, Y., Limin, G., Real-time Object Tracking Based on Improved Adversarial Learning, IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2020.
Ye, J., Stewart, E., Zhang, D., Chen, Q., Thangaraj, K., Roberts, C., Integration of Multiple Sensors for Noncontact Rail Profile Measurement and Inspection, IEEE Transactions on Instrumenttation and Measurement, 2021.
Meng, Y., Chen, F., Hao, X., Liu, Y., Jiang, J., Research on Rail Profile Meaurement Method Based on Machine Vision, 2020.
C. E. Shannon, A Mathematical Theory of Communication, The Bell System Technical Journal, 1948.
Dong, Z., Zhao, Y., Chen, Z., Support Vector Machine for Channel Prediction in High-Speed Railway Communication Systems, IEEE MTT-S International Wireless Symposium (IWS), 2018.
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Copyright (c) 2023 Kağan Murat, Mehmet Karaköse, Erhan Akın
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The articles published in International Journal of Computer and Information Technology (IJCIT) is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
