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

• URL: http://arxiv.org/abs/2609.07515v1
• Date: Mon, 07 Sep 2026 14:01:18 GMT
• Title: PICANet: Physics-Informed Cascaded Asymmetric Network for Infrared Small Target Detection
• Abstract summary: Infrared small target detection (ISTD) is an important research direction in image processing.<n>Existing methods are limited by severe background noise propagation and target degradation in high-level semantic features.<n>This paper proposes a plug-and-play physics-informed cascaded asymmetric network, named PICANet.
• Score: 18.422983137700392
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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