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Related Concept Videos

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Orthogonal Trajectories

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Related Experiment Video

Updated: May 28, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Autonomous UAV Target Search Method Based on Lightweight YOLOv8n and Coverage Path Planning.

Haoyan Duan1, Zhenhua Wang1, Mengtong Li2

  • 1School of Artificial Intelligence, China University of Geosciences, Beijing 100083, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient autonomous search method for Unmanned Aerial Vehicles (UAVs) using lightweight target detection and coverage path planning. The approach enhances search efficiency and accuracy for resource-constrained platforms.

Keywords:
UAVbreadth-first searchgreedy algorithmobject detectionpath planning

Related Experiment Videos

Last Updated: May 28, 2026

Computer Vision-Based Biomass Estimation for Invasive Plants
08:47

Computer Vision-Based Biomass Estimation for Invasive Plants

Published on: February 9, 2024

Area of Science:

  • Robotics
  • Computer Vision
  • Artificial Intelligence

Background:

  • Unmanned Aerial Vehicles (UAVs) are crucial for tasks like disaster search and rescue, but face computational and coverage limitations.
  • Efficient autonomous target search is a key challenge for UAVs in complex environments.

Purpose of the Study:

  • To develop an autonomous UAV search method combining lightweight target detection and coverage path planning.
  • To address computational constraints and improve environmental coverage efficiency for UAVs.

Main Methods:

  • Lightweight target recognition using pruned and quantized YOLOv8n with HSV data augmentation.
  • Dual-layer path planning: spatial coverage via Breadth-First Search (BFS) with greedy optimization and A* for backtracking.
  • Construction of a grid-based search environment model.

Main Results:

  • The proposed method significantly enhances UAV target search efficiency and reduces path redundancy.
  • Recognition accuracy is maintained even in complex environments.
  • The approach proves effective for autonomous UAV search on resource-constrained platforms.

Conclusions:

  • The integrated approach offers an effective solution for autonomous UAV target search.
  • Optimized target detection and path planning improve mission performance.
  • This method enables efficient autonomous operations for UAVs with limited onboard resources.