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Maritime Small Target Image Detection Algorithm Based on Improved YOLOv11n.

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Summary
This summary is machine-generated.

This study introduces an improved YOLOv11n algorithm for detecting small maritime targets in infrared and visible light images, significantly reducing missed detections and enhancing feature extraction for better sea surveillance.

Keywords:
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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Maritime Surveillance

Background:

  • Small ships in complex open-sea environments present detection challenges due to their size and limited features.
  • High missed detection rates hinder effective maritime surveillance and safety.

Purpose of the Study:

  • To develop an improved YOLOv11n algorithm for enhanced detection of small maritime targets.
  • To address limitations in feature information and reduce missed detections in infrared and visible light imagery.

Main Methods:

  • Introduced the BIE module for progressive fusion of infrared and visible light target features.
  • Integrated RepViTBlock with C3k2 module (C3k2-RepViTBlock) to improve tiny target feature capture.
  • Embedded ConvAttn module for adaptive contour feature extraction of small targets.

Main Results:

  • The improved algorithm demonstrated increased mAP@0.5 by 1.9% and 1.7% on IVships and SeaShips datasets, respectively.
  • Achieved improvements in average precision by 2.2% and 2.4% on the respective datasets.
  • Showcased enhanced small target detection capabilities with minimal parameter increase.

Conclusions:

  • The enhanced YOLOv11n algorithm effectively improves maritime small target detection accuracy.
  • The integration of BIE, C3k2-RepViTBlock, and ConvAttn modules offers a robust solution for complex maritime environments.
  • The algorithm provides a lightweight yet powerful tool for maritime surveillance and safety applications.