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Related Experiment Video

Updated: Nov 24, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Study on Visual Detection Algorithm of Sea Surface Targets Based on Improved YOLOv3.

Tao Liu1, Bo Pang1, Shangmao Ai2

  • 1College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin 150001, China.

Sensors (Basel, Switzerland)
|December 23, 2020
PubMed
Summary

This study introduces novel anchor-setting methods and a cross PANet structure to enhance sea target detection accuracy. The improved YOLO v3 model significantly boosts performance in complex marine environments.

Keywords:
YOLO v3anchor-settingbuoysconnection of cross-feature mapsfeature fusionshipstarget detection

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Area of Science:

  • Computer Vision
  • Marine Technology
  • Artificial Intelligence

Background:

  • Marine security is a growing global concern, necessitating accurate sea target detection.
  • Traditional YOLO v3 anchor-setting methods limit accuracy in complex marine environments due to incomplete feature map utilization.

Purpose of the Study:

  • To develop efficient and accurate sea-surface target detection algorithms.
  • To improve upon the limitations of existing YOLO v3 anchor-setting strategies for marine applications.

Main Methods:

  • Proposed two new anchor-setting methods: the average method and the select-all method.
  • Developed a cross PANet feature fusion structure for enhanced YOLO v3.
  • Conducted experiments using SeaBuoys and SeaShips datasets, combining methods with focal loss.

Main Results:

  • Achieved significant improvements in YOLO v3 accuracy for sea-surface target detection.
  • Attained a maximum mean Average Precision (mAP) of 98.37% on the SeaBuoys dataset.
  • Reached a maximum mAP of 90.58% on the SeaShips dataset.

Conclusions:

  • The proposed anchor-setting methods and cross PANet structure effectively enhance sea target detection.
  • The improved YOLO v3 demonstrates superior performance in complex marine surveillance scenarios.