Abstract:
Railway transport suffers from a major problem which is accidents, where the losses in hu-man lives and the materials are enormous.
To solve this problem or at least reduce the number of accidents, railway safety has proposednumerous initiatives with different methods and technologies. In this work, a method usingartificial intelligence is proposed.
The system developed must comply with numerous conditions contrary to the systems de-signed for the autonomous vehicle sector. One of the key challenges is long-range obstacledetection. Sensor technology in current land transport research is able to look 200 m ahead.
A system that combines two types of cameras; RGB and thermal; is suggested to consider thedifferent requirements. these ones consist of the different illumination and weather conditions,and also the range needed to detect obstacle on time.
This work is divided into two parts. The first one is the obstacle detection. To do so,three common object detection algorithms have been evaluated and compared (effectivenessand efficiency comparison). The algorithm with the lowest FAR( false Alarm rate), the bestDR (Detection Rate), and that achieves the shortest execution time has been chosen. Theobjective is to build a reliable system that can operate in real-time.The second part focuses on the estimation of the distance between objects and the train usingthe DisNet algorithm. Here, two evaluation methods have been used in order to assess theprecision of the estimation.
