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R. Kageyama, "Obstacle Detection using Camera and LiDAR for Train Forward Surveillance", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 11.6, 2024, doi:10.4203/ccc.7.11.6

Keywords: train forward surveillance, camera, LiDAR, deep learning, point cloud processing, sensor fusion.

Abstract

For the further improving the railway safety, we are developing a train forward surveillance method using cameras and sensors. In train forward surveillance, it is important to detect obstacles entering the tracks at a distance. Therefore, we investigated the combination of images obtained from cameras and point cloud data obtained from LiDAR as a suitable senser configuration for the railway environment. In our proposed method, the detection area is set by predicting the area of the rail track from the image, and objects such as people and automobiles are recognized using the deep learning. In addition, by combining detection using point cloud data, we can avoid missing objects even in conditions where recognition from images is difficult, such as at nighttime.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 7 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 11.6 Obstacle Detection using Camera and LiDAR for Train Forward Surveillance R. Kageyama Information and Communication Technology Division, Railway Technical Research Institute, Tokyo, Japan doi:10.4203/ccc.7.11.6 Full Bibliographic Reference for this paper R. Kageyama, "Obstacle Detection using Camera and LiDAR for Train Forward Surveillance", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 11.6, 2024, doi:10.4203/ccc.7.11.6 Keywords: train forward surveillance, camera, LiDAR, deep learning, point cloud processing, sensor fusion. Abstract For the further improving the railway safety, we are developing a train forward surveillance method using cameras and sensors. In train forward surveillance, it is important to detect obstacles entering the tracks at a distance. Therefore, we investigated the combination of images obtained from cameras and point cloud data obtained from LiDAR as a suitable senser configuration for the railway environment. In our proposed method, the detection area is set by predicting the area of the rail track from the image, and objects such as people and automobiles are recognized using the deep learning. In addition, by combining detection using point cloud data, we can avoid missing objects even in conditions where recognition from images is difficult, such as at nighttime. download the full-text of this paper (PDF, 12 pages, 1008 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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