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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Maritime Safety

Background:

  • UAV-based maritime rescue object detection requires both high accuracy and real-time performance.
  • Existing YOLO-based methods often sacrifice speed for accuracy or are difficult to implement.
  • There's a need for efficient and adaptable solutions for maritime search and rescue.

Purpose of the Study:

  • To develop a modular, plug-and-play optimization for the YOLOv8 framework.
  • To enhance real-time performance and accuracy for UAV maritime rescue object detection.
  • To create a flexible and extendable solution for practical applications.

Main Methods:

  • Implementation of a modular optimization strategy on the YOLOv8 architecture.
  • Utilizing the SeaDronesSee dataset for large-scale experimentation.
  • Comparative analysis against baseline YOLOv8x and existing two-stage detectors.

Main Results:

  • Achieved a 13.53% accuracy improvement over YOLOv8x.
  • Reduced computational cost by 85.63%.
  • Exceeded detection speed of a two-stage detector by over 20 times with comparable accuracy.

Conclusions:

  • The proposed modular approach effectively balances accuracy and real-time performance for UAV maritime rescue.
  • The method offers flexibility, ease of implementation, and extensibility.
  • Dataset analysis revealed object-specific detection challenges and potential biases.