Railway Corridor Monitoring Using Deep Drone Vision

The emergence of low-cost commercial drones fitted with a camera are ideal platforms for remotely monitoring critical assets such as railway corridor. The proposed system employs drones to automate and make the process efficient. In this paper, a railway monitoring system capable of detection and classification of various railway-related infrastructures such as lines, ballast, anchors, sleepers and fasteners using visual images captured by a drone is proposed. The first stage of classification uses a deep network that helps in qualifying the presence of track in a given frame. The second stage helps in classification of objects within a frame for further analysis. Two different deep architectures are used in classification of railway infrastructure—the first for offline analysis that uses transfer learning using a pre-trained GoogLeNet model and the second approach that uses a new architecture for embedded implementation. Transfer learning results in an overall f-score of 89%, and the new architecture results in an overall f-score of 81% with at least 10\(\times \) reduction in parameters.