DisNet: A novel method for distance estimation from monocular camera

In this paper, a machine learning setup that provides the obstacle detection system with a method to estimate the distance from the monocular camera to the object viewed with the camera is presented. In particular, the preliminary results of on-going research to allow the onboard multisensory system, which is under development within H2020 Shift2Rail project SMART, to autonomously learn distances to objects, possible obstacles on the rail tracks ahead of the locomotive are given. The presented distance estimation system is based on Multi Hidden-Layer Neural Network, named DisNet, which is used to learn and predict the distance between the object and the camera sensor. The DisNet was trained using a supervised learning technique where the input features were manually calculated parameters of the object bounding boxes resulted from the YOLO object classifier and outputs were the accurate 3D laser scanner measurements of the distances to objects in the recorded scene. The presented DisNet-based distance estimation system was evaluated on the images of railway scenes as well as on the images of a road scene. Shown results demonstrate a general nature of the proposed DisNet system that enables its use for the estimation of distances to objects imaged with different types of monocular cameras.