Intelligent aerial surveillance for safer railways using machine learning

Nagarathna C R ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 5, 1160-1166

Abstract: The integrity and usability of the rail systems are seriously compromised by problems including broken welds, unseen blockages, and non-functioning rails. Since they are primarily manual and offer no real-time information about what is happening, the present inspection methods are extremely labor-intensive, especially when it comes to remote and inaccessible places. The suggested system is a drone-based railway track surveillance system that can identify anomalies like fractures, welding flaws, and obstacles in real time. High-resolution camera drones and artificial intelligence (AI) models such as YOLO gather and evaluate data in a variety of environmental settings, and the problems they identify are geotagged. Resilient data transmission is ensured via a hybrid 4G/5G and LoRa network. Actionable insights and abnormalities are visualized and shown on a real-time dashboard. By accurately, scalably, and robustly observing the railway, the system increases maintenance efficiency.

Keywords: Computer vision; Drone equipped with AI; Real-time defect detection; Train safety. (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijirss.com/index.php/ijirss/article/view/9077/2027 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:aac:ijirss:v:8:y:2025:i:5:p:1160-1166:id:9077

Access Statistics for this article

International Journal of Innovative Research and Scientific Studies is currently edited by Natalie Jean

More articles in International Journal of Innovative Research and Scientific Studies from Innovative Research Publishing
Bibliographic data for series maintained by Natalie Jean ().