Efficient Maritime Traffic Flow Analysis Using YOLOv8 and DeepSORT for Vessel Detection and Tracking
This study presents a novel method for vessel traffic flow analysis based on video image recognition, utilizing the YOLOv8 object detection algorithm and the DeepSORT multi-object tracking technique. By integrating these advanced technologies, the proposed method achieves efficient and accurate detection and continuous tracking of vessels, enabling effective statistical analysis of vessel traffic flow and motion trajectories. The process begins with the YOLOv8 algorithm, which identifies vessel targets in video frames. Subsequently, the DeepSORT algorithm is employed to track the detected vessels continuously. Finally, statistical analysis of the tracked vessels provides critical information, such as vessel traffic flow and motion trajectories. This information is valuable for applications in vessel traffic management, safety monitoring, and related fields. The proposed method offer a robust solution for vessel traffic flow analysis.
Authors
- Xiaofeng Zou (ORCID: https://orcid.org/0009-0002-4651-2389)
- Xiang Zhong (ORCID: https://orcid.org/0000-0003-4501-4724)
- Xinglong Liu
- Yongtao Wang (ORCID: https://orcid.org/0000-0003-1379-2206)
- W.-Y. Wang
Institutions
- Minjiang University (CN)
- Institute of Navigation (US)
Publication Details
- Published
- 2025-08-08
- DOI
- https://doi.org/10.1109/iaeac65194.2025.11166335
- Primary Topic
- Maritime Navigation and Safety
- Type
- article
- Field-Weighted Citation Impact
- 0.00