Marine Object Detection Using LiDAR on an Unmanned Surface Vehicle
Marine object detection plays a crucial role in various applications such as collision avoidance and autonomous navigation in maritime environments. While most existing datasets focus on 2D object detection, this research introduces a novel 3D object detection approach that relies exclusively on LiD...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11075785/ |
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| author | Yvan Eustache Cedric Seguin Antoine Pecout Alexandre Foucher Johann Laurent Dominique Heller |
| author_facet | Yvan Eustache Cedric Seguin Antoine Pecout Alexandre Foucher Johann Laurent Dominique Heller |
| author_sort | Yvan Eustache |
| collection | DOAJ |
| description | Marine object detection plays a crucial role in various applications such as collision avoidance and autonomous navigation in maritime environments. While most existing datasets focus on 2D object detection, this research introduces a novel 3D object detection approach that relies exclusively on LiDAR (Light Detection And Ranging) data, specifically tailored for small Unmanned Surface Vehicles (USVs), where energy efficiency and computational constraints are key challenges. This study contributes a new point cloud dataset collected from a 2-meter autonomous USV and augmented through a hardware-in-the-loop simulation environment. The PointPillars network, chosen for its efficiency in processing LiDAR data, was trained and evaluated in this maritime context. A comparative analysis was also conducted between the proposed LiDAR-only method and a multimodal (LiDAR-camera) approach. The core innovation of this work is a step for late fusion strategy, where object detection is performed independently across sensors before integration. This results in a significantly less resource-intensive solution compared to early fusion methods. Consequently, the LiDAR-only approach highly suitable for deployment on compact, low-power autonomous surface drones, marking a step forward in practical and scalable marine perception systems. |
| format | Article |
| id | doaj-art-e438f7a3ec6f4044b1013d8b49e11144 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e438f7a3ec6f4044b1013d8b49e111442025-08-20T03:51:29ZengIEEEIEEE Access2169-35362025-01-011312165812166910.1109/ACCESS.2025.358731511075785Marine Object Detection Using LiDAR on an Unmanned Surface VehicleYvan Eustache0https://orcid.org/0009-0007-6769-2581Cedric Seguin1Antoine Pecout2Alexandre Foucher3https://orcid.org/0009-0006-5723-9516Johann Laurent4https://orcid.org/0000-0001-7133-759XDominique Heller5Lab-STICC UMR 6285, Université Bretagne Sud, Lorient, FranceLab-STICC UMR 6285, Université Bretagne Sud, Lorient, FranceDiades Marine, Brest, FranceLab-STICC UMR 6285, Université Bretagne Sud, Lorient, FranceLab-STICC UMR 6285, Université Bretagne Sud, Lorient, FranceLab-STICC UMR 6285, Université Bretagne Sud, Lorient, FranceMarine object detection plays a crucial role in various applications such as collision avoidance and autonomous navigation in maritime environments. While most existing datasets focus on 2D object detection, this research introduces a novel 3D object detection approach that relies exclusively on LiDAR (Light Detection And Ranging) data, specifically tailored for small Unmanned Surface Vehicles (USVs), where energy efficiency and computational constraints are key challenges. This study contributes a new point cloud dataset collected from a 2-meter autonomous USV and augmented through a hardware-in-the-loop simulation environment. The PointPillars network, chosen for its efficiency in processing LiDAR data, was trained and evaluated in this maritime context. A comparative analysis was also conducted between the proposed LiDAR-only method and a multimodal (LiDAR-camera) approach. The core innovation of this work is a step for late fusion strategy, where object detection is performed independently across sensors before integration. This results in a significantly less resource-intensive solution compared to early fusion methods. Consequently, the LiDAR-only approach highly suitable for deployment on compact, low-power autonomous surface drones, marking a step forward in practical and scalable marine perception systems.https://ieeexplore.ieee.org/document/11075785/Deep learninghardware-in-the-loop simulatorLiDAR datasetmarine object detectionPointPillarsROS |
| spellingShingle | Yvan Eustache Cedric Seguin Antoine Pecout Alexandre Foucher Johann Laurent Dominique Heller Marine Object Detection Using LiDAR on an Unmanned Surface Vehicle IEEE Access Deep learning hardware-in-the-loop simulator LiDAR dataset marine object detection PointPillars ROS |
| title | Marine Object Detection Using LiDAR on an Unmanned Surface Vehicle |
| title_full | Marine Object Detection Using LiDAR on an Unmanned Surface Vehicle |
| title_fullStr | Marine Object Detection Using LiDAR on an Unmanned Surface Vehicle |
| title_full_unstemmed | Marine Object Detection Using LiDAR on an Unmanned Surface Vehicle |
| title_short | Marine Object Detection Using LiDAR on an Unmanned Surface Vehicle |
| title_sort | marine object detection using lidar on an unmanned surface vehicle |
| topic | Deep learning hardware-in-the-loop simulator LiDAR dataset marine object detection PointPillars ROS |
| url | https://ieeexplore.ieee.org/document/11075785/ |
| work_keys_str_mv | AT yvaneustache marineobjectdetectionusinglidaronanunmannedsurfacevehicle AT cedricseguin marineobjectdetectionusinglidaronanunmannedsurfacevehicle AT antoinepecout marineobjectdetectionusinglidaronanunmannedsurfacevehicle AT alexandrefoucher marineobjectdetectionusinglidaronanunmannedsurfacevehicle AT johannlaurent marineobjectdetectionusinglidaronanunmannedsurfacevehicle AT dominiqueheller marineobjectdetectionusinglidaronanunmannedsurfacevehicle |