DGNet: Dual-knowledge Guided Network for Infrared Small Target Detection

2026-08-31

AI Summary

为解决红外小目标检测中的背景抑制与目标增强矛盾及部署约束问题，提出基于多通用文本的DGNet网络，通过PWM模块和CDA损失函数优化模型。

Abstract

InfRared Small Target Detection (IRSTD) is a prominent and challenging task in computer vision. In recent years, text-guided methods have significantly improved detection performance. However, they still suffer from two key limitations. First, a single text description simultaneously modeling both background and target leads to semantic entanglement, which contradicts the objective of background suppression and target enhancement. Second, reliance on image-specific textual prompts (requiring additional external models such as CLIP during inference) results in deployment constraints. To address these issues, we propose a novel Dual-knowledge Guided Network (DGNet) based on multiple generalizable texts. Specifically, we design a Prior-knowledge Wavelet Modulation (PWM) module, which leverages dual textual priors that separately characterize large-scale backgrounds and sparse targets to effectively disentangle and modulate entangled semantics in the frequency domain. Furthermore, we introduce a Consensus-knowledge Directional Alignment (CDA) loss, which models the initial state and the ideal target across samples as `complex background' and `bright target', respectively, thereby constructing a clear and unified directional optimization trajectory for the model. Extensive experiments on three public datasets demonstrate the superior performance of DGNet and the effectiveness of each component. The source code is available at https://github.com/iLearn-Lab/MM26-DGNet.

Problem

• InfRared Small Target Detection
• semantic entanglement
• deployment constraints

Innovation

• Dual-knowledge Guided Network
• Prior-knowledge Wavelet Modulation
• Consensus-knowledge Directional Alignment