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Related Experiment Video

Updated: Aug 16, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Real-Time Detection of Drones Using Channel and Layer Pruning, Based on the YOLOv3-SPP3 Deep Learning Algorithm.

Xuetao Zhang^1,2, Kuangang Fan^2,3,4, Haonan Hou^2,3

¹School of Mechanical and Electrical Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China.

|December 23, 2022

Summary

This study introduces an optimized YOLOv3-SPP3 algorithm for real-time drone detection. The enhanced model significantly improves detection speed and accuracy, crucial for security applications.

Keywords:

YOLOv3-SPP3 anti-drone feature extraction pruning algorithm real-time detection

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Area of Science:

Computer Vision
Artificial Intelligence
Aerospace Engineering

Background:

Real-time drone detection is critical for securing high-risk areas.
Challenges include drone size, speed, and environmental interference.
Existing methods struggle with rapid and accurate identification.

Purpose of the Study:

To develop an efficient and accurate drone detection algorithm.
To overcome limitations of current drone detection technologies.
To enable real-time interception of unauthorized drones.

Main Methods:

Utilized k-means clustering for bounding box labeling.
Applied channel and shortcut layer pruning to YOLOv3-SPP3.
Fine-tuned the pruned model for enhanced real-time performance.

Main Results:

Achieved a maximum compression factor of 20.1x.
Increased detection speed by up to 10.2x.
Improved mean Average Precision (mAP) by 15.2% and precision by 16.54%.

Conclusions:

The optimized YOLOv3-SPP3 algorithm offers superior performance over existing methods.
Achieved 95.15% mAP and 112 f/s detection speed, meeting real-time UAV detection needs.
Demonstrated significant advancements in drone detection efficiency and accuracy.