Abstract
Vessel detection, classification, and tracking are very important problems in maritime surveillance systems. In recent years, the field of computer vision has significantly developed, which allows its application to these systems. Accordingly, in this paper, a method based on a YOLOv5-based deep neural network combined with the Strong Simple Online Real-time Object Tracking (StrongSORT) algorithm is proposed for vessel detection, classification, and tracking. Specifically, the YOLOv5 model is trained by using a dataset of diverse images, which are collected from various public sources. The dataset contains several popular vessel types for the purpose of classification. Experimental results show that the proposed model gives high accuracy of vessel classification and high-speed tracking of approximately 16 frames per second, which is near real-time. The model has been embedded into a real small demonstrator to verify the potential implementation in maritime surveillance systems.
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Pham, QH., Doan, VS., Pham, MN., Duong, QD. (2023). Real-Time Multi-vessel Classification and Tracking Based on StrongSORT-YOLOv5. In: Nguyen, T.D.L., Verdú, E., Le, A.N., Ganzha, M. (eds) Intelligent Systems and Networks. ICISN 2023. Lecture Notes in Networks and Systems, vol 752. Springer, Singapore. https://doi.org/10.1007/978-981-99-4725-6_17
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DOI: https://doi.org/10.1007/978-981-99-4725-6_17
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