{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T12:10:51Z","timestamp":1780402251932,"version":"3.54.1"},"reference-count":35,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T00:00:00Z","timestamp":1780358400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the rapid development of intelligent navigation technologies, accurate dynamic object detection in complex maritime environments remains a critical challenge due to occlusion, scale variation, and multi-target interference. To address these issues, this study proposes a vision\u2013radar fusion-based dynamic object detection method. A cross-modal feature mapping mechanism is developed to achieve deep integration of visual and radar information, and an augmented Lagrangian optimization strategy is introduced to enhance feature consistency and representation capability. Furthermore, an improved Faster R-CNN framework is designed by optimizing the region proposal network and incorporating a multi-scale training strategy to improve detection performance for objects of varying scales. Experimental results on a self-constructed MVRD show that the proposed method achieves detection accuracies of 88.93%, 76.86%, 74.47%, and 83.01% under sunny, strong illumination, foggy, and crossing-waterway conditions, respectively. These results demonstrate that the proposed approach exhibits strong robustness and stability in complex maritime environments. Overall, the method significantly improves dynamic object detection accuracy and provides effective support for reliable environmental perception in intelligent navigation systems.<\/jats:p>","DOI":"10.3390\/s26113508","type":"journal-article","created":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T11:25:31Z","timestamp":1780399531000},"page":"3508","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dynamic Object Detection in Maritime Navigation Scenarios Based on Vision\u2013Radar Fusion"],"prefix":"10.3390","volume":"26","author":[{"given":"Qianqian","family":"Chen","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Big Data, Wuhan Business University, Wuhan 430056, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9286-2094","authenticated-orcid":false,"given":"Changshi","family":"Xiao","sequence":"additional","affiliation":[{"name":"School of Navigation, Wuhan University of Technology, Wuhan 430060, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bowei","family":"Li","sequence":"additional","affiliation":[{"name":"China Mobile Group Hubei Co., Ltd., Wuhan 420010, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,6,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114205","DOI":"10.1016\/j.oceaneng.2023.114205","article-title":"A real-time multi-ship collision avoidance decision-making system for autonomous ships considering ship motion uncertainty","volume":"278","author":"Zhang","year":"2023","journal-title":"Ocean Eng."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"17917","DOI":"10.1109\/TITS.2024.3419046","article-title":"Dynamic adaptive decision-making method for autonomous navigation of ships in coastal waters","volume":"25","author":"Zhao","year":"2024","journal-title":"IEEE Trans. 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