Rail Guard: Intelligent Rail Inspection using YOLOv3

Indian railways are the largest mode of transport for Indians due to massive infrastructure. Around 24 million commuters use train regularly as a daily use. The system need to be automatically analyze recent accidents and customer complaints to ensure safe and secured operations. The significance of accident analysis is to detect cracks or disjoints in the rail structure to avoid derailments resulting in a heavy loss of life and property. The rail crack identification method using emerging techniques may help to automate the earlier recovery of system. The paper proposes a Railway Track Fault detection system, featuring an IoT-controlled rover equipped with You Only Look Once version 3 (YOLOv3), represents a significant leap forward in railway safety and maintenance. With a commendable accuracy of 75% and operating at 10 RPM, the rover captures high-quality frames, facilitating precise fault detection, including disjoint and misalignment issues. In addition to YOLOv3's prowess, our system integrates GPS-based location tracking to pinpoint fault locations accurately. Moreover, leveraging Twilio messaging integration enables real-time communication of fault locations to relevant authorities or in-charge personnel. In summary, the combination of YOLOv3 technology, GPS-based location tracking, and Twilio messaging capabilities in the proposed IoT-controlled rover offers a comprehensive solution for proactive railway track fault detection and management.