发表机构
Shanghai University; School of Microelectronics, Shanghai University; School of Mechatronic Engineering and Automation, Shanghai University; School of Intelligent Systems Engineering, Sun Yat-sen University（上海大学; 上海大学微电子学院; 上海大学机电工程与自动化学院; 中山大学智能工程学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对红外小目标检测中背景噪声传播与目标退化问题，提出物理信息级联非对称网络PICANet，通过分层先验解耦、双先验交互融合及级联非对称注意力模块，提升复杂背景下检测精度。
AI 中文摘要
红外小目标检测（ISTD）是图像处理中的一个重要研究方向。然而，现有方法受到严重背景噪声传播和高层语义特征中目标退化的限制。为了解决这些局限性，本文提出了一种即插即用的物理信息级联非对称网络，命名为PICANet。具体而言，我们构建了一个分层先验解耦模块，以显式提取低层和高层的物理信息，从而在不同层次上表征目标特征，而非仅依赖卷积提取。此外，开发了一个双先验交互融合模块，以动态细化目标表示，同时抑制复杂的背景杂波。与以往工作不同，我们引入了一个具有级联非对称机制的多层交叉特征注意力模块，以实现高层语义与低层空间细节之间的精确对齐。大量实验表明，所提出的PICANet优于最先进的ISTD方法，即使在复杂背景下也展现出令人满意的检测精度。我们的代码可在以下网址获取：https URL。
英文摘要
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.
