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Related Experiment Video

Updated: Sep 1, 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

High-Resolution Drone Detection Based on Background Difference and SAG-YOLOv5s.

Yaowen Lv¹, Zhiqing Ai¹, Manfei Chen¹

¹College of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun 130022, China.

Sensors (Basel, Switzerland)

|August 12, 2022

Summary

This study introduces SAG-YOLOv5s, a novel drone detection method for high-resolution images. It significantly improves accuracy and speed for fixed-camera surveillance systems.

Keywords:

background difference drone high-resolution image object detection small target

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Last Updated: Sep 1, 2025

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Area of Science:

Computer Vision
Artificial Intelligence
Surveillance Technology

Background:

Traditional drone detection methods struggle with low accuracy and slow speeds in high-resolution images.
Fixed-camera surveillance systems require efficient and precise drone detection capabilities.

Purpose of the Study:

To develop an accurate and fast drone detection method for high-resolution images captured by fixed cameras.
To improve upon existing YOLOv5s performance by integrating novel techniques.

Main Methods:

A hybrid approach combining background difference and a lightweight network (SAG-YOLOv5s).
Integration of Ghost module and SimAM attention mechanism to optimize YOLOv5s.
Utilization of α-DIoU loss for enhanced bounding box regression accuracy.
Creation of a high-resolution drone dataset for validation.

Main Results:

The proposed SAG-YOLOv5s method achieved a detection accuracy of 97.6%.
Demonstrated a significant improvement of 24.3 percentage points over standard YOLOv5s.
Achieved a detection speed of 13.2 FPS in 4K video, meeting practical demands.

Conclusions:

The SAG-YOLOv5s method offers a superior balance between detection accuracy and speed for high-resolution drone detection.
This approach provides a benchmark for fixed-camera drone detection systems.
The method effectively reduces computational overhead while enhancing feature extraction.