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Lightweight high-precision SAR ship detection method based on YOLOv7-LDS.

Shiliang Zhu1,2, Min Miao1,2

  • 1School of Communication, Beijing Information Science and Technology University, Beijing, China.

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Summary
This summary is machine-generated.

This study introduces YOLOv7-LDS, a lightweight Synthetic Aperture Radar (SAR) ship detection model balancing accuracy and efficiency. It achieves high performance on benchmark datasets, offering a practical solution for maritime monitoring.

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Area of Science:

  • Computer Vision and Image Processing
  • Remote Sensing and Geospatial Analysis
  • Artificial Intelligence and Machine Learning

Background:

  • Synthetic Aperture Radar (SAR) ship detection faces challenges with target size variations and high computational costs, limiting practical applications.
  • Existing object detection models often struggle to balance accuracy with the efficiency required for deployment on resource-constrained platforms like satellites or drones.

Purpose of the Study:

  • To develop a lightweight and accurate SAR ship detection model, YOLOv7-LDS, addressing the limitations of current methods.
  • To enhance detection precision and computational efficiency for practical deployment in maritime surveillance.

Main Methods:

  • Introduced a streamlined feature extraction network based on Shufflenetv2 with Squeeze-and-Excitation (SE) attention.
  • Developed the Weighted Efficient Aggregation Network (DCW-ELAN) in the Neck section, utilizing Coordinate Attention (CA) and Depthwise Convolution (DWConv).
  • Incorporated a lightweight Spatial Pyramid Dilated Convolution Cross-Stage Partial Channel (LSPHDCCSPC) module for multi-scale feature extraction.

Main Results:

  • YOLOv7-LDS achieved a Mean Average Precision (mAP) of 99.1% on the SAR Ship Detection Dataset (SSDD) and 95.8% on NWPU VHR-10.
  • The model boasts a low parameter count (3.4 million), Giga Floating Point Operations Per Second (GFLOPs) of 6.1, and an Inference Time (IT) of 4.8 milliseconds.
  • Demonstrated a superior balance between computational cost and detection performance compared to state-of-the-art object detection models.

Conclusions:

  • YOLOv7-LDS offers a highly efficient and accurate solution for SAR ship detection.
  • The model's lightweight design and strong performance make it suitable for real-time maritime ship monitoring on airborne or satellite platforms.
  • This research provides a resilient and effective tool for enhancing maritime surveillance capabilities.