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An Improved YOLOv5-Based Lightweight Submarine Target Detection Algorithm.

Likun Mei1, Zhili Chen1

  • 1School of Optoelectronic Engineering, Xi'an Technological University, Xi'an 710021, China.

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Summary
This summary is machine-generated.

This study introduces an improved YOLOv5 algorithm for lightweight submarine recognition, enhancing maritime security. The new method boosts detection accuracy and precision while significantly reducing computational load for mobile deployment.

Keywords:
C3_DSSA-netlight weight

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Defense Technology

Background:

  • Traditional submarine recognition algorithms struggle with feature representation and robustness.
  • Deploying deep learning for submarine detection on embedded systems is challenging.

Purpose of the Study:

  • To develop a lightweight, high-precision submarine automatic recognition detection algorithm based on YOLOv5.
  • To improve the efficiency and accuracy of submarine recognition for maritime security applications.

Main Methods:

  • An improved YOLOv5 architecture incorporating MobileNetV3-based Feature Pyramid and C3_DS module.
  • Integration of an adaptive neck from SA-net strategy to minimize missed detections.
  • Evaluation on a dedicated submarine dataset.

Main Results:

  • Achieved increases of 8.54% in Precision, 6.02% in Recall, and 3.36% in mAP0.5.
  • Reduced parameter quantity by 34.1% and computational complexity by 67.9%.
  • Demonstrated significant improvements in detection accuracy and robustness.

Conclusions:

  • The proposed lightweight YOLOv5 model offers enhanced submarine recognition capabilities.
  • The method effectively addresses limitations of traditional algorithms and deployment challenges on mobile platforms.
  • This approach significantly contributes to maritime security and military defense through improved target detection.