Ship detection and tracking based on an improved YOLO11n and ByteTrack framework
DOI:
https://doi.org/10.54939/1859-1043.j.mst.CSCE9.2025.111-122Keywords:
Ship detection; Ship tracking; YOLO11n; SEAM; CCFM; ByteTrackAbstract
Ship detection and tracking are critical components of intelligent maritime and coastal surveillance systems. This study introduces a robust method for multi-class vessel detection and real-time tracking. The approach improves the YOLO11n model by incorporating the Separated and Enhancement Attention Module (SEAM) and the Cross-scale Channel Fusion Module (CCFM) into the neck, which enhances multi-scale feature aggregation and edge attention. Experimental results demonstrate an increase in mAP50 from 94.26% to 94.84% and in mAP50–95 from 69.59% to 70.40% on a custom ship dataset. The model size increases only slightly, while real-time inference speed remains at 25 FPS on the Jetson Xavier NX. A custom dataset comprising over 7,600 manually labeled images was developed, covering six vessel categories: cargo ship, passenger ship, military ship, sailboat, fishing boat, and patrol ship. Images were sourced from various public repositories to ensure diversity in vessel size, viewing angle, background, and lighting conditions. The complete system integrates the enhanced YOLO11n with the ByteTrack algorithm for real-time vessel detection and tracking. Experimental results confirm the system's feasibility for practical maritime surveillance applications.
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