FSGNet: A Frequency-Aware and Semantic Guidance Network for Infrared Small Target Detection

TL;DR

FSGNet introduces a frequency-aware and semantic guidance framework for infrared small target detection, improving localization accuracy and robustness by integrating multi-scale frequency filtering and semantic flow mechanisms.

Contribution

The paper proposes FSGNet, a novel lightweight detection network that combines frequency-aware filtering and semantic guidance to enhance small target detection accuracy.

Findings

Achieves superior detection performance on four datasets.

Maintains high efficiency and robustness.

Effectively suppresses background interference.

Abstract

Infrared small target detection (IRSTD) aims to identify and distinguish small targets from complex backgrounds. Leveraging the powerful multi-scale feature fusion capability of the U-Net architecture, IRSTD has achieved significant progress. However, U-Net suffers from semantic degradation when transferring high-level features from deep to shallow layers, limiting the precise localization of small targets. To address this issue, this paper proposes FSGNet, a lightweight and effective detection framework incorporating frequency-aware and semantic guidance mechanisms. Specifically, a multi-directional interactive attention module is proposed throughout the encoder to capture fine-grained and directional features, enhancing the network's sensitivity to small, low-contrast targets. To suppress background interference propagated through skip connections, a multi-scale frequency-aware module leverages Fast Fourier transform to filter out target-similar clutter while preserving salient target structures. At the deepest layer, a global pooling module captures high-level semantic information, which is subsequently upsampled and propagated to each decoder stage through the global semantic guidance flows, ensuring semantic consistency and precise localization across scales. Extensive experiments on four public IRSTD datasets demonstrate that FSGNet achieves superior detection performance and maintains high efficiency, highlighting its practical applicability and robustness. The codes will be released on https://github.com/Wangtao-Bao/FSGNet.