A single infrared small target detection method combining frequency domain saliency enhancement and spatial detail focusing

By combining frequency domain saliency enhancement and spatial detail focusing, the problem of poor background modeling and noise suppression in infrared small target detection is solved, achieving high-precision and low-false-alarm infrared small target detection.

CN122244499APending Publication Date: 2026-06-19ZHEJIANG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
ZHEJIANG UNIV
Filing Date
2026-02-05
Publication Date
2026-06-19

AI Technical Summary

⚠Technical Problem

Infrared small target detection suffers from problems such as poor background modeling and noise suppression in complex backgrounds, spatial information loss in deep learning methods, feature degradation, and false bright spot detection, resulting in insufficient detection stability.

⚗Method used

By combining frequency domain saliency enhancement and spatial detail focusing methods, high-precision detection of small infrared targets is achieved through the coding module, cross-scale feature interaction module, and decoding and reconstruction module of a U-Net-like network.

🎯Benefits of technology

It improves the accuracy and stability of infrared small target detection, reduces the false alarm rate, and is suitable for infrared small target detection in complex backgrounds.

✦ Generated by Eureka AI based on patent content.

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Abstract

This invention discloses a single-frame infrared small target detection method combining frequency domain saliency enhancement and spatial detail focusing, relating to the fields of infrared image processing and target detection technology. The method includes frequency domain adaptive enhancement and spatial detail focusing steps, implemented based on a U-Net-like network: an encoding module extracts multi-level features and enhances the saliency of small targets through frequency domain transformation, frequency masking, and channel interaction; a cross-scale feature interaction module achieves multi-scale information complementarity; and a decoding and reconstruction module focuses on small target details through channel attention, local refinement operators, and gated residual modulation. This invention overcomes the problems of poor background suppression in traditional methods and feature degradation in deep learning methods, achieving high-precision, low-false-alarm detection of infrared small targets in complex backgrounds, and is suitable for scenarios such as infrared search and tracking, and early warning detection.
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