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Lightweight visual localization algorithm for UAVs.

Yuhang Wang^1,2, Xuefeng Feng³, Feng Li³

¹College of Computer Science and Technology, Xinjiang University, Urumqi, 830046, China.

Scientific Reports

|February 19, 2025

Summary

This summary is machine-generated.

This study introduces the Lightv8nPnP algorithm, a lightweight deep learning model for drone visual positioning. It achieves high accuracy and speed, significantly reducing computational load for enhanced drone navigation.

Keywords:

Deep learning Lightweight Vision-based positioning

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Area of Science:

Robotics
Computer Vision
Artificial Intelligence

Background:

Deep learning models for drone visual positioning often face challenges with computational complexity and accuracy.
Existing algorithms may struggle with custom datasets containing imbalanced sample difficulty and varying pixel quality.

Purpose of the Study:

To develop a lightweight and efficient visual positioning algorithm for drones.
To improve the accuracy and speed of 3D drone positioning using deep learning.

Main Methods:

Introduced GhostConv into the detection head to create the GDetect module, reducing complexity.
Employed Wise-IoU as the bounding box regression loss function to handle dataset challenges.
Modified the YOLOv8n network structure to create TrimYOLO, reducing redundant feature maps.

Main Results:

The Lightv8nPnP algorithm demonstrated reduced parameters and computational load compared to benchmarks.
Achieved a high detection rate of 186 frames per second.
Maintained a 3D positioning error of less than 5.5 centimeters on X, Y, and Z axes.

Conclusions:

Lightv8nPnP offers an efficient solution for lightweight drone visual positioning.
The proposed optimizations enhance model performance and accuracy for aerial datasets.
The algorithm provides a viable option for real-time, precise drone navigation.