Railway Track Monitoring: Advanced Approaches Using Artificial Intelligence and Industrial Vision

Railways play a crucial role in the large-scale transportation of passengers and goods. With a network spanning approximately 5,000 km and the Algerian government’s objective to expand it for greater economic and industrial development, rail infrastructure requires continuous monitoring. Railway tracks are subjected to significant mechanical stresses, which can lead to various surface defects (DSR) such as cracks, corrugations, and squats. These defects result from material fatigue, wear, or dynamic loads, representing a major safety risk that can cause severe accidents such as derailments.

This project adopts an industrial approach to improve the precision and robustness of automated track inspection processes while optimizing maintenance costs and enhancing railway safety. By leveraging industrial vision techniques and artificial intelligence (AI), the project aims to automate inspections with real-time monitoring, overcoming the limitations of traditional methods such as ultrasonic testing, eddy currents, or laser-based inspections.

Compared to conventional techniques, vision-based monitoring offers significant advantages, including non-contact inspection, increased flexibility, faster execution, reduced costs, and automation potential. The project focuses on developing robust AI-based models capable of detecting, identifying, and classifying subtle and complex track defects under varying environmental conditions, ensuring safer, more reliable, and cost-effective railway operations.