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Updated: May 10, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Improved Aerial Surface Floating Object Detection and Classification Recognition Algorithm Based on YOLOv8n.

Lili Song1,2, Haixin Deng1,2, Jianfeng Han1,2

  • 1School of Information Engineering, Inner Mongolia University of Technology, Jinchuan Campus, Hohhot 010080, China.

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|April 28, 2025
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Summary
This summary is machine-generated.

This study introduces an improved YOLOv8-HSH algorithm for detecting small floating objects in complex aerial images. The enhanced model significantly boosts detection accuracy and robustness, offering better environmental monitoring solutions.

Keywords:
aerial photographenvironmental monitoringfloating object recognitionsmall object detection

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Area of Science:

  • Computer Vision
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Detecting small floating objects in aerial images is challenging due to minimal feature representation and complex water surface environments.
  • Existing methods struggle with accuracy and robustness in identifying diverse floating objects.

Purpose of the Study:

  • To develop an improved object detection algorithm for enhanced identification of water surface floating objects.
  • To increase the accuracy and robustness of detecting small and varied-sized floating objects in aerial imagery.

Main Methods:

  • An enhanced YOLOv8-HSH algorithm was proposed, building upon YOLOv8n.
  • Key enhancements include an improved HorBlock module, an optimized CBAM attention mechanism, a minor target recognition layer, and the WIoU loss function.

Main Results:

  • The proposed algorithm achieved significant improvements: mAP50 increased by 11.7%, mAP50-95 by 12.4%, and the miss rate decreased by 11%.
  • The F1 score rose by 11%, with a minimum 5.6% average accuracy increase per object category.

Conclusions:

  • The improved YOLOv8-HSH algorithm demonstrates superior detection accuracy and robustness in complex aerial imaging scenarios.
  • This research offers advanced solutions for aerial image processing and environmental monitoring, particularly for water surface object detection.