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A reliable unmanned aerial vehicle multi-ship tracking method.

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This study enhances ship detection and tracking for unmanned aerial vehicles (UAVs) using improved AI algorithms. The system boosts accuracy in monitoring maritime traffic, crucial for global logistics.

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Area of Science:

  • Artificial Intelligence
  • Computer Vision
  • Maritime Logistics

Background:

  • Waterway transportation is vital for global logistics, necessitating accurate ship detection and tracking.
  • Unmanned aerial vehicles (UAVs) offer potential for enhanced surveillance but face challenges with data limitations and detection accuracy.
  • Existing multi-object tracking systems require improvements for real-world maritime monitoring applications.

Purpose of the Study:

  • To develop an advanced multi-object tracking system for UAV-based ship detection and tracking.
  • To improve the accuracy and robustness of ship detection using enhanced YOLOv7.
  • To refine the tracking performance of the Deep SORT algorithm for maritime scenarios.

Main Methods:

  • Utilized YOLOv7 for object detection and Deep SORT for multi-object tracking.
  • Employed transfer learning to address limited ship data for YOLOv7 training.
  • Integrated SimAM attention mechanism and partial convolution (PConv) module into YOLOv7 for improved feature extraction.
  • Replaced IOU with DIOU metric in Deep SORT to reduce false negatives during track matching.

Main Results:

  • The enhanced YOLOv7 model with SimAM and PConv demonstrated superior feature representation and detection capabilities.
  • The modified Deep SORT algorithm with DIOU metric improved track matching and reduced missed detections.
  • The integrated system achieved a 6.9% increase in Multi-Object Tracking Accuracy (MOTA) to 65.3% and a 3.0% increase in Multi-Object Tracking Precision (MOTP) to 81.9% compared to the baseline YOLOv7+Deep SORT.
  • The system showed robust performance in ship monitoring, outperforming the original model significantly.

Conclusions:

  • The developed AI system offers a significant advancement in UAV-based ship detection and tracking.
  • The integration of SimAM, PConv, and DIOU metric provides a more accurate and efficient solution for maritime surveillance.
  • This research provides a valuable reference for enhancing the capabilities of autonomous systems in critical logistics and security applications.