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Real-Time Recognition Algorithm of Small Target for UAV Infrared Detection.

Qianqian Zhang^1,2, Li Zhou¹, Junshe An^1,3

¹National Space Science Center, Chinese Academy of Sciences, Beijing 101499, China.

Sensors (Basel, Switzerland)

|May 25, 2024

Summary

This study enhances Unmanned Aerial Vehicle (UAV) infrared detection for small, weak targets. The improved method boosts real-time performance and accuracy, crucial for effective aerial surveillance.

Keywords:

UAV detection infrared picodet real-time small target

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Area of Science:

Computer Vision
Artificial Intelligence
Remote Sensing

Background:

Unmanned Aerial Vehicle (UAV) infrared detection faces challenges with small, weak targets and complex backgrounds.
Existing target detection algorithms struggle to meet high detection rates, low missed detection rates, and real-time performance demands.
These limitations hinder effective aerial surveillance and reconnaissance applications.

Purpose of the Study:

To develop an improved small target detection method for UAV infrared imagery.
To enhance real-time detection performance and reduce false and missed detection rates.
To address the specific challenges of weak and small target identification in complex environments.

Main Methods:

An improved lightweight LCNet network was integrated as the backbone for feature extraction to enhance real-time performance.
The Squeeze-and-Excitation module was incorporated to improve detection of weak targets.
The feature pyramid structure was optimized to further refine detection capabilities.
The proposed method is based on the Picodet architecture.

Main Results:

The improved model demonstrated a significant increase in real-time frame rate by 31 frames per second (fps).
The mean average precision (MAP) saw an improvement of 7%, indicating enhanced detection accuracy.
Experimental validation was conducted on the HIT-UAV public dataset.

Conclusions:

The proposed enhanced detection method effectively addresses the limitations of existing algorithms for UAV infrared small target detection.
The improvements in real-time performance and accuracy are critical for practical applications in aerial surveillance.
This research contributes a robust solution for challenging infrared detection scenarios.