PICANet: Physics-Informed Cascaded Asymmetric Network for Infrared Small Target Detection

2026-09-07

AI Summary

为解决红外小目标检测中的背景噪声和目标退化问题，提出了一种基于物理信息的级联非对称网络PICANet，通过分层先验解耦和双先验交互融合等模块提高检测精度。

Abstract

Infrared small target detection (ISTD) is an important research direction in image processing. However, existing methods are limited by severe background noise propagation and target degradation in high-level semantic features. To address these limitations, this paper proposes a plug-and-play physics-informed cascaded asymmetric network, named PICANet. Specifically, we construct a hierarchical prior decoupling module to explicitly extract low-level and high-level physical information, thereby characterizing target features at different levels rather than relying solely on convolutional extraction. Furthermore, a dual-prior interactive fusion module is developed to dynamically refine target representations while suppressing complex background clutter. Unlike previous work, a multi-level cross-feature attention module with the cascaded asymmetric mechanism is introduced to achieve precise alignment between high-level semantics and low-level spatial details. Extensive experiments demonstrate that the proposed PICANet outperforms state-of-the-art ISTD methods, showing satisfactory detection accuracy even against complex backgrounds. Our code is available at https://github.com/xianchaoxiu/PICANet.

Problem

• Infrared small target detection
• background noise propagation
• target degradation
• high-level semantic features

Innovation

• Physics-informed
• Cascaded Asymmetric Network
• Hierarchical Prior Decoupling Module
• Dual-prior Interactive Fusion Module
• Multi-level Cross-feature Attention