eprintid: 10197444 rev_number: 8 eprint_status: archive userid: 699 dir: disk0/10/19/74/44 datestamp: 2024-09-25 12:29:22 lastmod: 2024-09-25 12:30:34 status_changed: 2024-09-25 12:29:22 type: article metadata_visibility: show sword_depositor: 699 creators_name: Xie, Yongchang creators_name: Nanlal, Cassandra creators_name: Liu, Yuanchang title: Reliable LiDAR-based ship detection and tracking for Autonomous Surface Vehicles in busy maritime environments ispublished: pub divisions: UCL divisions: B04 divisions: F45 keywords: LiDAR-based perception; Ship detection; Deep learning; Object tracking; Autonomous Surface Vehicle note: Copyright © 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). abstract: Environmental perception is a crucial requirement of Autonomous Surface Vehicles (ASVs) if required to perform tasks safely in a dynamically complex operational environment. Most existing methods for ship detection rely on camera-based methods, which are sensitive to environmental conditions and cannot directly provide spatial location information related to detected targets. To overcome this limitation, we propose a LiDAR-based ship detection and tracking framework that can be applied to busy maritime environments. The proposed framework consists of two functional modules: a ship detection and multi-object tracking. For ship detection, a modularised network structure was adapted, allowing for ease of switching between different types of detection network to prioritise either detection accuracy, detection speed or a compromise of both, depending on the task requirements. A Kalman Filter-based multi-object tracking method is also implemented to compensate for any detections that may have been missed as a result of ship motions or occlusions, relying solely on the detection results. We also collected the first-ever real-world LiDAR dataset for maritime applications across the River Thames and marinas, including a range of ship types, with lengths ranging from 5 m up to 40 m, and different hull types. The datasets are organised in a similar manner to the KITTI datasets, which can be easily applied to the well-developed point cloud detection networks. Remarkably, our methods achieve an overall detection accuracy of 74.1% in the collected datasets. The proposed framework and dataset make LiDAR-based environmental perception feasible for implementation in ASVs and support development in the autonomous maritime navigation field. date: 2024-11-15 date_type: published publisher: Elsevier official_url: https://doi.org/10.1016/j.oceaneng.2024.119288 oa_status: green full_text_type: pub language: eng primo: open primo_central: open_green verified: verified_manual elements_id: 2319716 doi: 10.1016/j.oceaneng.2024.119288 lyricists_name: Liu, Yuanchang lyricists_id: YLIUA09 actors_name: Liu, Yuanchang actors_id: YLIUA09 actors_role: owner full_text_status: public publication: Ocean Engineering volume: 312 number: Part-3 article_number: 119288 issn: 0029-8018 citation: Xie, Yongchang; Nanlal, Cassandra; Liu, Yuanchang; (2024) Reliable LiDAR-based ship detection and tracking for Autonomous Surface Vehicles in busy maritime environments. Ocean Engineering , 312 (Part-3) , Article 119288. 10.1016/j.oceaneng.2024.119288 <https://doi.org/10.1016/j.oceaneng.2024.119288>. Green open access document_url: https://discovery.ucl.ac.uk/id/eprint/10197444/1/Reliable%20LiDAR-based%20ship%20detection%20and%20tracking%20for%20Autonomous%20Surface%20Vehicles%20in%20busy%20maritime%20environments.pdf