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Infrared dim tiny-sized target detection based on feature fusion.

Peng Zhang¹, Yaman Jing^2,3, Guodong Liu^2,3

¹College of Information Science and Engineering, Fujian Key Laboratory of Light Propagation and Transformation, Huaqiao University, Xiamen, 361021, Fujian, China.

Scientific Reports

|February 5, 2025

Summary

This study introduces a novel network for detecting dim, tiny infrared targets. The dual-layer omnidirectional target enhancement (DODTE) and residual-based pyramid-like (RBPL) modules improve detection accuracy for challenging infrared imaging scenarios.

Keywords:

Dual-layer omnidirectional target enhancement Feature fusion enhancement Infrared dim tiny-sized target detection Pyramid-like module

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Published on: March 6, 2019

Area of Science:

Computer Vision
Infrared Imaging
Object Detection

Background:

Infrared object detection is crucial for remote sensing and thermal imaging.
Detecting distant, small, low-intensity infrared targets presents significant challenges due to low contrast and ambiguous shapes.

Purpose of the Study:

To develop an effective method for detecting infrared dim tiny-sized targets.
To address issues of low contrast, lost position information, and ambiguous target shapes in infrared images.

Main Methods:

Proposed a feature fusion network incorporating a dual-layer omnidirectional target enhancement (DODTE) module to improve position information extraction.
Introduced a residual-based pyramid-like (RBPL) module to extract deep shape information, compensating for fixed convolution kernel limitations.
Evaluated the network on open-source datasets and experimentally generated infrared images.

Main Results:

The proposed network demonstrated superior performance compared to existing algorithms.
Quantitative metrics, including Intersection over Union (IOUs) and F1 scores, validated the network's effectiveness.
Ablation experiments confirmed the efficacy of the DODTE and RBPL modules in handling tiny size, low contrast, and dark intensity targets.

Conclusions:

The developed feature fusion network effectively detects infrared dim tiny-sized targets.
The DODTE and RBPL modules provide a robust solution for challenging infrared detection tasks.
The study offers a significant advancement in the field of infrared object detection.