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Related Experiment Video

Updated: Jun 6, 2025

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Electroantennography-based Bio-hybrid Odor-detecting Drone using Silkmoth Antennae for Odor Source Localization

Published on: August 27, 2021

DRBD-YOLOv8: A Lightweight and Efficient Anti-UAV Detection Model.

Panpan Jiang^1,2,3, Xiaohua Yang^2,4, Yaping Wan^2,4

¹School of Nuclear Science and Technology, University of South China, Hengyang 421001, China.

Sensors (Basel, Switzerland)

|November 27, 2024

Summary

This summary is machine-generated.

A new lightweight deep learning model, DRBD-YOLOv8, enhances anti-unmanned aerial vehicle (UAV) detection. It achieves high accuracy and speed for edge devices, overcoming limitations of current systems.

Keywords:

BiFPN YOLOv8n anti-UAV detection edge-computing devices lightweight loss function

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Area of Science:

Computer Vision
Artificial Intelligence
Aerospace Engineering

Background:

Growing security and privacy concerns necessitate effective anti-unmanned aerial vehicle (UAV) systems.
Existing deep learning models struggle to balance accuracy, speed, and resource efficiency for edge-based anti-UAV detection.

Purpose of the Study:

To develop a lightweight and efficient anti-UAV detection model for real-time applications on edge devices.
To improve the balance between detection accuracy, processing speed, and model size.

Main Methods:

Proposed DRBD-YOLOv8 model incorporating Re-parameterization Cross-Stage Efficient Layered Attention Network (RCELAN) and Bidirectional Feature Pyramid Network (BiFPN).
Introduced a novel DN-ShapeIoU loss function to boost detection accuracy.
Utilized depthwise separable convolutions to reduce computational complexity.

Main Results:

DRBD-YOLOv8 demonstrated superior performance over YOLOv8n in mAP50, mAP95, precision, and FPS.
The model achieved a significantly reduced GFLOPs and parameter count, with a file size of 3.25 M.
Achieved a near 50% reduction in model size compared to YOLOv8n.

Conclusions:

DRBD-YOLOv8 offers a compelling solution for real-time anti-UAV detection on resource-constrained edge devices.
The model's lightweight design, high accuracy, and fast processing speed make it suitable for practical security applications.