A radar–vision fusion approach for ship monitoring in bridge waterways using weakly supervised cross-modal representation learning

Frequent ship–bridge collisions pose serious risks to infrastructure, public safety, and economic assets. Reliable ship perception is critical for timely risk identification in bridge waterways. Radar–vision fusion enables complementary ship sensing capabilities beyond single-sensor monitoring using cameras or radars alone. However, existing rule-based fusion methods exhibit limited applicability in continuous waterway monitoring due to poor generalisation and labour-intensive calibration. This study proposes a weakly supervised representation learning framework that achieves embedding-level alignment with reduced manual supervision to improve the robustness of radar–vision association-based ship monitoring. A time window-based pseudo-labelling strategy is introduced to guide a Transformer-based dual encoder for learning shared radar–vision representations, with a novel ordinal loss incorporated into contrastive learning to enforce physical consistency. The framework is validated on a real-world bridge waterway using a field-deployed radar–camera system, achieving an F1-score of 95.7% and outperforming state-of-the-art learning-based fusion methods in the traffic sensing domain. The proposed approach can provide reliable and automated situational awareness of bridge waterway traffic to support decision-making in navigational safety management systems.

• Record URL:
  • https://doi.org/10.1016/j.oceaneng.2026.128262
• Record URL:
  • http://www.sciencedirect.com/science/article/pii/S0029801826040965
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00298018
• Supplemental Notes:
  • © 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies. Abstract reprinted with permission of Elsevier.
• Authors:
  • Liao, Ruixuan
  • Zhang, Yiming
  • Zhao, Tianhao
  • Miao, Maoqin
  • Mao, Jianxiao
  • Wang, Hao
• Publication Date: 2026-11-15

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: 128262
• Serial:
  • Ocean Engineering
  • Volume: 368
  • Issue Number: 0
  • Publisher: Pergamon
  • ISSN: 0029-8018
  • EISSN: 1873-5258
  • Serial URL: http://www.sciencedirect.com/science/journal/00298018

Subject/Index Terms

• TRT Terms: Bridges; Data fusion; Machine learning; Machine vision; Maritime safety; Monitoring; Navigation; Radar; Safety management; Ships
• Subject Areas: Data and Information Technology; Marine Transportation; Vehicles and Equipment;

Filing Info

• Accession Number: 02004875
• Record Type: Publication
• Files: TRIS
• Created Date: Oct 2 2026 4:54PM