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An Improved YOLOv8 OBB Model for Ship Detection through Stable Diffusion Data Augmentation.

Sang Feng1, Yi Huang1, Ning Zhang1

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This study introduces a novel data augmentation technique using stable diffusion and an enhanced YOLOv8n OBB model for improved unmanned aerial vehicle (UAV) ship detection. The methods effectively address multi-viewpoint and multi-scale challenges in real-time ship management.

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BiFPN structureEMA moduleYOLOv8multi-scale detectionship detectionstable diffusion

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Maritime Technology

Background:

  • Unmanned aerial vehicles (UAVs) offer advanced capabilities for real-time ship detection and management.
  • Existing ship detection methods face challenges with multi-viewpoint, multi-scale imagery, environmental variability, and limited datasets.

Purpose of the Study:

  • To enhance ship detection accuracy and efficiency using UAVs.
  • To address data scarcity and improve model performance for multi-viewpoint and multi-scale ship instances.

Main Methods:

  • A data augmentation method based on stable diffusion was proposed to expand datasets.
  • The YOLOv8n OBB model was improved by integrating the BiFPN structure and EMA module.
  • Comparative experiments were conducted to evaluate the proposed data augmentation and model.

Main Results:

  • The stable diffusion-based data augmentation proved effective for low-volume datasets with complex features.
  • The enhanced YOLOv8n-BiFPN-EMA OBB model achieved high performance in detecting multi-viewpoint and multi-scale ships.
  • The improved model demonstrated a mAP (@0.5) of 92.3% and mAP (@0.5:0.95) of 77.5%, with reduced parameters and real-time detection speed.

Conclusions:

  • The proposed data augmentation method significantly enhances datasets for UAV-based ship detection.
  • The improved YOLOv8n-BiFPN-EMA OBB model offers a robust solution for real-time, multi-viewpoint, and multi-scale ship detection.
  • This research contributes to more effective and efficient maritime surveillance and management systems.