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Related Experiment Video

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Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
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Ship detection based on semantic aggregation for video surveillance images with complex backgrounds.

Yongmei Ren1, Haibo Liu1, Jie Yang2

  • 1School of Electrical and Information Engineering, Hunan Institute of Technology, Hengyang, China.

Peerj. Computer Science
|February 3, 2025
PubMed
Summary

This study introduces an advanced ship detection method for complex backgrounds, achieving high accuracy and real-time performance. The technique enhances feature fusion for precise identification of vessels in surveillance imagery.

Keywords:
Feature fusionImage processingSemantic aggregationShip detectionVideo surveillance images

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Area of Science:

  • Computer Vision and Image Processing
  • Artificial Intelligence
  • Machine Learning for Surveillance

Background:

  • Ship detection in video surveillance is crucial but challenging due to complex backgrounds.
  • Existing methods struggle to balance detection precision and speed in varied environments.

Purpose of the Study:

  • To develop an effective ship detection method for complex backgrounds.
  • To improve both the accuracy and speed of ship identification in video surveillance.

Main Methods:

  • A semantic aggregation module merges deep and shallow features for enhanced representation.
  • A multiscale object detection layer improves detection across various ship sizes.
  • Distance Intersection over Union (DIoU) loss function refines detection precision.

Main Results:

  • Achieved a mean average precision@0.5 (mAP@0.5) of 89.30% on the SeaShips dataset.
  • Attained an mAP@0.5 of 89.10% on the SeaShips_enlarge dataset.
  • Demonstrated superior detection performance compared to existing techniques.

Conclusions:

  • The proposed method offers superior detection effects for ships in complex backgrounds.
  • The technique meets real-time detection requirements, highlighting its practical engineering value.