A Model for Detecting Movement in Railway Infrastructure Using Deep Convolutional Neural Network
Keywords:
Image Processing, Pattern Recognition, Movement Detection, Railway Infrastructure, DCNN
Abstract
The continuous monitoring of movements using artificial intelligence technique has become a necessary system to cob the level crossing problems faced around the railway infrastructure. This project work has developed a model for detecting movements using image processing method with convolutional neural network, an artificial intelligence technique in monitoring and detection of movements in the railway infrastructure. Images containing movements and non-movements in railway infrastructure were acquired from Google image search have been used in the implementation of the system and a deep convolutional neural network model has been developed. The proposed model was able to classify 11 out of 12 images correctly in the evaluation of the train dataset and the performance accuracy of the model recorded was 92% with a loss of 0.4% which resulted to the misclassification of one of the images. The analysis of the system showed that the system can be optimized to perform better.
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References
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2. Amaral, V., Marques, F., Lourenço, A., Barata, J., & Santana, P. (2016). Laser-based obstacle detection at railway level crossings. Journal of Sensors, 2016.
3. Andrade, A. R. (2008). Renewal decisions from a Life-cycle Cost (LCC) Perspective in Railway Infrastructure: An integrative approach using separate LCC models for rail and ballast components. M. Sc. Thesis.
4. Andrusca, M., Adam, M., Dragomir, A., Lunca, E., Seeram, R., & Postolache, O. (2020). Condition monitoring system and faults detection for impedance bonds from railway infrastructure. Applied Sciences, 10(18), 6167.
5. Arastounia, M. (2015). Automated recognition of railroad infrastructure in rural areas from LiDAR data. Remote Sensing, 7(11), 14916-14938.
6. Asian Development Bank (2017). Unlocking the potential of railways: A railway strategy for CAREC, 2017–2030, DOI: http://dx.doi.org/10.22617/RPT178656-2
7. Chang, L., Dollevoet, R. P. B. J., & Hanssen, R. F. (2014). Railway infrastructure monitoring using satellite radar data. International Journal of Railway. Technology, 3(2), 79-91.
8. Dewangan, R. K., & Chouhan, Y. (2020). A Review on Object Detection using Open CV Method.International Research Journal of Engineering and Technology (IRJET), 07(09), 3859-3861.
9. Doll, C., Schade, W., & Rothengatter, W. (2015). The Results and Efficiency of Railway Infrastructure Financing within the EU. European Parliament, Directorate General for Internal Policies, Policy Department D: Budgetary Affairs. Karlsruhe, Brussels.
10. Elberinka, S. O., Khoshelham, K., Arastounia, M., & Benito, D. D. (2013). Rail track detection and modelling in mobile laser scanner data. ISPRS International Architecture for Photogrammetry, Remote Sensing and Spatial Information Science, 5(2), 223-228.
11. Hijazi, S., Kumar, R., & Rowen, C. (2015). Using convolutional neural networks for image recognition. Cadence Design Systems Inc.: San Jose, CA, USA, 1-12.
12. Hromádka, V., Korytárová, J., Vítková, E., Seelmann, H., & Funk, T. (2020). New aspects of socioeconomic assessment of the railway infrastructure project life cycle. Applied Sciences, 10(20), 7355.
13. Irianto, K. D., Ariyanto, G., & Ary, D. (2009). Motion Detection Using OpenCV with Background Subtraction and Frame Differencing Technique. Simposium Nasional RAPI VIII, 74-81.
14. Jhapate, A. K., Malviya, S., & Jhapate, M. (2020, February). Unusual Crowd Activity Detection using OpenCV and Motion Influence Map. In 2nd International Conference on Data, Engineering and Applications (IDEA) (pp. 1-6). IEEE.
15. Kovacevic, M. S., Car, M., Bacic, M., Stipanovic, I., Gavin, K., Noren-Cosgriff, K., & Kaynia, A. (2018). Report on the Use of Remote Monitoring for Slope Stability Assessments; H2020-MG 2014–2015 Innovations and Networks Executive Agency.
16. Lidén, T. (2015). Railway infrastructure maintenance-a survey of planning problems and conducted research. Transportation Research Procedia, 10, 574-583.
17. Oraegbune, O. M., & Ugwu, O. O. (2020). Delivering sustainable transport infrastructure projects (railway) in Nigeria: Frameworks, indicators, method and tools. Nigerian Journal of Technology, 39(3), 665-679.
18. Oraegbune, O. M., & Ugwu, O. O. (2020). Delivering sustainable transport infrastructure projects (railway) in Nigeria: Frameworks, indicators, method and tools. Nigerian Journal of Technology, 39(3), 665-679.
19. Park, G., Lee, M., Jang, H., & Kim, C. (2021). Thermal anomaly detection in walls via CNN-based segmentation. Automation in Construction, 125, 103627.
20. Parveen, S., & Shah, J. (2021, February). A Motion Detection System in Python and Opencv. In 2021 Third International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV) (pp. 1378-1382). IEEE.
21. Ravish Aradhya, H. V. (2019). Object detection and tracking using deep learning and artificial intelligence for video surveillance applications. International Journal of Advanced Computer Science and Applications, 10(12), 517-530.
22. Ristic-Durrant, D., Franke, M., & Michels, K. (2021). A review of vision-based on-board obstacle detection and distance estimation in railways. Sensors, 21(10), 3452.
23. Sahebdivani, S., Arefi, H., & Maboudi, M. (2020). Rail Track Detection and Projection-Based 3D Modeling from UAV Point Cloud. Sensors, 20(18), 5220.
24. Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN Computer Science, 2(3), 1-21.
25. Sattar, S., Li, S., & Chapman, M. (2018). Road surface monitoring using smartphone sensors: A review. Sensors, 18(11), 3845.
26. Soni, A. (2016). Non-contact monitoring of railway infrastructure with terrestrial laser scanning and photogrammetry at Network Rail (Doctoral dissertation, UCL (University College London)).
27. Stenström, C. (2014). Operation and maintenance performance of rail infrastructure: Model and Methods (Doctoral dissertation).
28. Vadlamudi, H. (2020). Evaluation of Object Tracking System using Open-CV In Python. International Journal of Engineering Research & Technology, 09(09), 394-399.
29. Wang, C., & Xi, Y. (1997). Convolutional neural network for image classification. Johns Hopkins University Baltimore, MD, 21218.
30. Weston, P., Roberts, C., Yeo, G., & Stewart, E. (2015). Perspectives on railway track geometry condition monitoring from in-service railway vehicles. Vehicle System Dynamics, 53(7), 1063-1091.
Published
2022-07-15
How to Cite
Nuka, N. D., & Daniel, O. W. (2022). A Model for Detecting Movement in Railway Infrastructure Using Deep Convolutional Neural Network. Central Asian Journal of Theoretical and Applied Science, 3(7), 122-132. Retrieved from https://cajotas.centralasianstudies.org/index.php/CAJOTAS/article/view/807
Section
Articles
