The railroad industry is crucial for modern transportation, therefore the need for maintaining the integrity and safety of rail infrastructure is immense. Traditional rail inspection methods, involving manual teams or specialized vehicles, are labor-intensive and costly, causing logistical inconveniences, especially for rapid, large-scale inspection. This paper explores and expands the adaptation of Unmanned Aerial Vehicles (UAVs) and advanced computer vision for rail inspection. While existing literature highlights the benefits and capabilities of UAVs, challenges persist, and a fully integrated, online system has yet to be thoroughly implemented and tested. We seek to create a system that performs the task of track following strictly by visual sensor perception, eliminating any reliance on GPS and ensuring autonomy in environments with limited or degraded GPS availability, such as dense settings, tunnels, etc. The system will perform all processing onboard, providing immediate results without the need for external processing or infrastructure. Our proposed approach divides the problem into track detection, track interpretation, and track following. This work focuses on the first of these steps, track detection. We survey existing approaches, assess their strengths and limitations, and introduce a novel method addressing prior challenges, keeping in mind the goal of a fully integrated, autonomous system for rapid track assessment.

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