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Heuristic data-driven anchor generation for UAV-based maritime rescue image object detection

  • 0School of Computer Science and Engineering, Northeastern University, Shenyang, China.
Heliyon +

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May 27, 2024

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May 27, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Optimizing anchor boxes in Unmanned Aerial Vehicle (UAV) maritime rescue image detection significantly boosts performance. This heuristic approach enhances target detection accuracy, crucial for effective drone-based rescue operations.

Area Of Science

  • Computer Vision and Image Analysis
  • Robotics and Autonomous Systems
  • Maritime Safety and Rescue Technologies

Background

  • Maritime rescue operations using Unmanned Aerial Vehicles (UAVs) face challenges in accurately detecting targets in complex visual data.
  • Current object detection models often struggle with the specific scenarios and tasks encountered in drone-based maritime surveillance.

Purpose Of The Study

  • To enhance the performance of object detection models for UAV-based maritime rescue by optimizing anchor box generation.
  • To investigate heuristic methods for extracting relevant data features from UAV maritime rescue imagery.

Main Methods

  • Utilized heuristic methods to extract data features from UAV maritime rescue images.
  • Optimized anchor box generation within the MMDetection object detection framework.
  • Conducted experiments on the large-scale SeaDronesSee maritime rescue dataset.

Main Results

  • Optimized anchor boxes improved model performance by 48.9% to 62.8% compared to default configurations.
  • The most proficient model exceeded the official SeaDronesSee baseline performance by over 49.3%.
  • Analysis identified variations in detection difficulty for different objects and explained underlying reasons.

Conclusions

  • The proposed heuristic method for anchor box optimization significantly improves UAV maritime rescue object detection.
  • The findings offer a promising approach for refining data analysis and enhancing maritime rescue capabilities.
  • This research contributes to the advancement of autonomous systems in critical search and rescue applications.

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