[1]XIA Guihua,BAI Jun,LI Shiyang,et al.Research on self-supervised heterogeneous dual-teacher distillation for ship detection in point clouds[J].CAAI transactions on intelligent systems,2026,21(5):1268-1281.[doi:10.11992/tis.202606023]
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Research on self-supervised heterogeneous dual-teacher distillation for ship detection in point clouds

CAAI transactions on intelligent systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 21 Number of periods: 2026 5 Page number: 1268-1281 Column: 学术论文—机器感知与模式识别 Public date: 2026-09-05
Title:
Research on self-supervised heterogeneous dual-teacher distillation for ship detection in point clouds
Author(s):
XIA Guihua1;  BAI Jun1;  LI Shiyang2;  LIANG Zhaowei3;  LI Lingya4
1. College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China;
2. School of Naval Architecture and Ocean Engineering, Dalian University of Technology, Dalian 116024, China;
3. Wuhan Second Ship Design and Research Institute, Wuhan 430060, China;
4. APT Mobile SatCom Limited, Shenzhen 518033, China
Keywords:
unmanned surface vehicle; ship point cloud; object detection; LiDAR; self-supervised learning; dual-teacher distillation; contrastive learning; feature transfer
CLC:
TP242
DOI:
10.11992/tis.202606023
Abstract:
With the rapid development of artificial intelligence, autonomous navigation and operation technologies for unmanned surface vehicle(USV) have become one of the current research focuses in academia. Maritime ship target detection and environmental perception are key technologies for ensuring autonomous decision-making and safe navigation of USVs. LiDAR can provide stable three-dimensional geometric information, which makes ship point-cloud target detection an important technical approach for maritime environmental perception. To address the problems of difficult construction of high-quality datasets, sparse echoes from long-range targets, and strong interference from dynamic sea clutter in maritime ship point-cloud target detection, this study proposed a distillation method guided by a self-supervised heterogeneous dual-teacher contrastive network. The method aims to reduce the dependence of ship point-cloud target detection networks on ship point-cloud datasets and improve their detection robustness under sparse target representation and complex maritime background interference. Experiments were conducted on a maritime ship point-cloud dataset collected by the “Dolphin-1” intelligent vessel. The results show that the proposed method improves the mean average precision (mAP) by 6.4 percentage points compared with the baseline method, maintains relatively stable detection performance in sparse point-cloud and strong-clutter scenes, and has application potential for deployment and optimization on onboard perception platforms of USVs.
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