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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM63666
Volume:
10 |
5 |
May 2026
Pages:
1-6
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Department of Master of Computer Applications
Bapuji Institute of Engineering and Technology,Davanagere
Department of Master of Computer Applications
Bapuji Institute of Engineering and Technology,Davanagere
Department of Master of Computer Applications
Bapuji Institute of Engineering and Technology,Davanagere
AI-Based Railway Infrastructure Monitor-ing & Alerting Solution is an advanced technology innovation that promises to change the face of railway infrastructures. Intelligent Railway Assets & Infrastructure Monitoring. The system functions as an in-telligent surveillance network capable of delivering "Condition Based Maintenance" approach in place of the conventional reac-tive approach in rail systems maintenance. Multi-modal data acquisition and edge computing. The system architecture ensures uninter-rupted transmission of high-quality teleme-try information thanks to the synergy be-tween the data from the different vehicle-mounted and trackside sensor nodes. Com-puter Vision Integration The architecture employs high-definition and high-speed op-tical sensors to continuously perform a “vi-sion-based checkup” of the entire railway corridor. Synchronized Sensing The vision-based sensing is supplemented by acoustic and vibration sensors capable of detecting vibration signatures indicative of fatigue or geometrical deformations within the tracks that would otherwise remain unnoticed by the human eye. Autonomous Anomaly De-tection and Diagnostics The core of the ar-chitecture lies in employing custom-de-signed neural networks capable of identify-ing structural abnormalities.
KEYWORDS
This project has introduced an AI-driven solution for the purpose of automated intel-ligent fault monitoring in railway infra-structure. Combining the benefits of a deep learning–based visual inspection system along with an automated web dashboard for real time viewing, our solution makes it possible to effectively detect faults in tracks, such as cracks, alignment issues and obstructions. The adoption of a modern Convolutional Neural Net-work and trans-fer learning makes this system extremely efficient. The system provides continuous moni-tor-ing capability and improved decision mak-ing with faster detection of faults through centralization of data visualization and analysis of past records. The system im-proves safety in the railway sector, mini-mizes cost of operations and helps maintain railways predictively in smart railway sys-tems. Therefore, the proposed Rail-Guard system is a viable and scalable option for intelligent monitoring of railways. The “AI-Based Railway Infrastructure Monitoring & Alert System” is more than an advancement in technology; it is a long-term investment in the future of railway transportation, ensuring that our railways will be safer, more dependable and more sustainable for years to come.
1. Zhang, X., et al. (2019). Vision-based rail crack detection using deep learning. 2. Smith, J., & Kumar, A. (2020). Predic-tive maintenance for railways: Sensor fu-sion and machine learning approaches. Journal of Rail and Rapid Transit. 3. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature. 4. Bishop, C. M. (2006). Pattern Recogni-tion and Machine Learning. Springer. 5. Szeliski, R. (2022). Computer Vision: Algorithms and Applications. Springer. 6. Kiran, B. R., Thomas, D. M., & Parakkal, R. (2018). An overview of deep learning-based methods for unsupervised and semi-supervised anomaly detection in videos. Journal of Imaging. 7. Li, H., et al. (2020). Intelligent fault de-tection for railway infrastructure using sen-sor networks and deep learning. IEEE Ac-cess.
Publication Date
5/21/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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