Target Ship Recognition and Tracking with Data Fusion Based on Bi-YOLO and OC-SORT Algorithms for Enhancing Ship Navigation Assistance

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Yayımlandı:Journal of Marine Science and Engineering vol. 13, no. 2 (2025), p. 366
Yazar: Chen, Shuai
Diğer Yazarlar: Gao, Miao, Shi, Peiru, Zeng, Xi, Zhang, Anmin
Baskı/Yayın Bilgisi:
MDPI AG
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035 |a 3171121206 
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100 1 |a Chen, Shuai 
245 1 |a Target Ship Recognition and Tracking with Data Fusion Based on Bi-YOLO and OC-SORT Algorithms for Enhancing Ship Navigation Assistance 
260 |b MDPI AG  |c 2025 
513 |a Journal Article 
520 3 |a 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. 
653 |a Navigation 
653 |a Accuracy 
653 |a Datasets 
653 |a Algorithms 
653 |a Binocular vision 
653 |a Tracking systems 
653 |a Data integration 
653 |a Navigation systems 
653 |a Tracking 
653 |a Localization 
653 |a Global positioning systems--GPS 
653 |a Sorting algorithms 
653 |a Ship accidents & safety 
653 |a Efficiency 
653 |a Position measurement 
653 |a Cameras 
653 |a Remote sensing 
653 |a Perceptions 
653 |a Maritime industry 
653 |a Trajectories 
653 |a Sensors 
653 |a Decision making 
653 |a Situational awareness 
653 |a Perception 
653 |a Object recognition 
653 |a Shipping industry 
653 |a Position errors 
653 |a Economic 
653 |a Environmental 
700 1 |a Gao, Miao 
700 1 |a Shi, Peiru 
700 1 |a Zeng, Xi 
700 1 |a Zhang, Anmin 
773 0 |t Journal of Marine Science and Engineering  |g vol. 13, no. 2 (2025), p. 366 
786 0 |d ProQuest  |t Engineering Database 
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