A railroad detection algorithm for infrastructure surveillance using enduring airborne systems
Infrastructure surveillance is an important requirement for many companies. With the advancement of technology, drones can now provide an efficient tool for such applications. A possible future scenario is the automated surveillance of railroads. Whereas numerous algorithms that provide railroad detection exist, they have mainly focused either on satellite images or for small, low altitude drones which are unsuitable for our particular scenario. In this paper we propose a railroad detection algorithm tailored for large, high altitude enduring drones. More specifically, we use Hough Transform to detect lines and perform a line clustering in the Rho and Theta space. A score model is also proposed in order to identify the railroad. We test our method on several sequences supplied by Airbus Defense & Space and show our algorithm to provide a detection rate of 93.23% in average.