Target Ship Recognition and Tracking with Data Fusion Based on Bi-YOLO and OC-SORT Algorithms for Enhancing Ship Navigation Assistance
保存先:
| 出版年: | Journal of Marine Science and Engineering vol. 13, no. 2 (2025), p. 366 |
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| 第一著者: | |
| その他の著者: | , , , |
| 出版事項: |
MDPI AG
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| 主題: | |
| オンライン・アクセス: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 抄録: | With the ever-increasing volume of maritime traffic, the risks of ship navigation are becoming more significant, making the use of advanced multi-source perception strategies and AI technologies indispensable for obtaining information about ship navigation status. In this paper, first, the ship tracking system was optimized using the Bi-YOLO network based on the C2f_BiFormer module and the OC-SORT algorithms. Second, to extract the visual trajectory of the target ship without a reference object, an absolute position estimation method based on binocular stereo vision attitude information was proposed. Then, a perception data fusion framework based on ship spatio-temporal trajectory features (ST-TF) was proposed to match GPS-based ship information with corresponding visual target information. Finally, AR technology was integrated to fuse multi-source perceptual information into the real-world navigation view. Experimental results demonstrate that the proposed method achieves a mAP0.5:0.95 of 79.6% under challenging scenarios such as low resolution, noise interference, and low-light conditions. Moreover, in the presence of the nonlinear motion of the own ship, the average relative position error of target ship visual measurements is maintained below 8%, achieving accurate absolute position estimation without reference objects. Compared to existing navigation assistance, the AR-based navigation assistance system, which utilizes ship ST-TF-based perception data fusion mechanism, enhances ship traffic situational awareness and provides reliable decision-making support to further ensure the safety of ship navigation. |
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| ISSN: | 2077-1312 |
| DOI: | 10.3390/jmse13020366 |
| ソース: | Engineering Database |