Railway Track Crack Detection Using Machine Learning

– Railway infrastructure maintenance is crucial for ensuring the safety and efficiency of rail transport systems. This project presents an innovative approach for the inspection of rail fasteners and crack detection using an embedded vision and sensor-based system integrated with an IoT (Internet of Things) vehicle. The system employs advanced image processing and machine learning algorithms to analyze pre-captured real-time images by high-resolution cameras and various onboard sensors. These images, along with sensor data, enable the detection of faults such as cracks, corrosion, wear, or misalignment in rail fasteners. The IoT vehicle, equipped with these technologies, autonomously travels along the railway track, capturing and transmitting data to a centralized cloud-based platform for analysis. Faults identified during the inspection are automatically flagged, allowing for immediate alerts and timely maintenance interventions. The proposed system not only enhances the efficiency and accuracy of rail inspections but also supports proactive maintenance, reducing the risk of failures and improving the overall safety of the railway network. The integration of embedded vision, sensors, and IoT technologies represents a significant advancement in the automation of railway infrastructure monitoring, offering a cost-effective and reliable solution for real-time fault detection and maintenance planning.