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YOLOv7-CSAW for maritime target detection.

Qiang Zhu1, Ke Ma1, Zhong Wang1

  • 1School of Computer Science and Technology, Hefei Normal University, Hefei, China.

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|July 20, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces YOLOv7-CSAW, an enhanced maritime search and rescue algorithm that significantly improves small target detection. The new model achieves higher accuracy and reduces false negatives in complex maritime environments.

Keywords:
ASFFSimAMWIoUYOLOv7maritime target detection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Marine Technology

Background:

  • Maritime search and rescue (SAR) operations face challenges with low detection rates and high false negatives due to complex environments and small targets.
  • Current target detection algorithms struggle with robustness and generalization in maritime SAR.

Purpose of the Study:

  • To develop an improved target detection algorithm for maritime SAR operations.
  • To enhance the accuracy and robustness of detecting small targets in challenging maritime conditions.

Main Methods:

  • Proposed YOLOv7-CSAW, an enhanced algorithm based on YOLOv7.
  • Incorporated K-means++ for anchor box optimization, C2f module for gradient flow, SimAM for small target perception, ASFF for feature fusion, and WIoU loss function.

Main Results:

  • YOLOv7-CSAW achieved a 10.73% improvement in mean average precision (mAP) over the YOLOv7 baseline.
  • Demonstrated significant improvements in detection performance compared to traditional deep learning algorithms on a maritime SAR dataset.

Conclusions:

  • YOLOv7-CSAW significantly enhances accuracy and robustness for small target detection in complex maritime scenes.
  • The algorithm effectively addresses issues in maritime SAR, improving detection rates and reducing false negatives.