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Real-Time Intelligent Detection Algorithm for Ship Targets in High-Resolution Wide-Swath Sea Surface Images Captured

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Summary

This study introduces an optimized YOLOv8 model for real-time ship detection in aerial images, enhancing maritime monitoring on embedded systems. The model achieves high accuracy while maintaining efficient processing speeds.

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embedded systemlightweight YOLOv8ship detectionwide-format aerial camera

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Area of Science:

  • Computer Vision
  • Maritime Surveillance
  • Deep Learning

Background:

  • Real-time ship detection in aerial imagery is crucial for maritime monitoring.
  • Challenges include large data volumes and significant scale variations in vessels.
  • Existing methods struggle with real-time performance on embedded platforms.

Purpose of the Study:

  • To develop an optimized YOLOv8-based model for efficient and accurate ship detection.
  • To enhance scale adaptability and reduce computational overhead for embedded systems.
  • To improve maritime monitoring capabilities through advanced aerial imagery analysis.

Main Methods:

  • An optimized YOLOv8 model incorporating a Multi-Scale Fusion (MSF) module in the backbone.
  • A lightweight Group-Wise Scale Fusion Neck (GSF-Neck) with a parallel multi-branch structure for adaptive feature fusion.
  • Deployment on an RK3588 embedded system utilizing a sliding window strategy.

Main Results:

  • Achieved a state-of-the-art mean Average Precision (mAP@0.5) of 94.55% on an aerial ship dataset.
  • Maintained a stable processing speed of ≥2 frames per second (fps) on the embedded system.
  • Demonstrated a 1.4% improvement in mAP with a 6.6% reduction in FPS compared to the baseline model.

Conclusions:

  • The proposed YOLOv8-based model effectively balances detection performance and computational efficiency for real-time maritime surveillance.
  • The integration of MSF and GSF-Neck modules significantly enhances scale adaptability and reduces computational load.
  • The model offers a viable solution for real-time ship detection on resource-constrained embedded platforms.