Progressive Fusion of Multi-Scale Mamba Context and Local Detail Priors for Infrared Small Target Detection

Infrared Small Target Detection (IRSTD) requires strong target-level detection capability, which depends on effective modeling of long-range global dependencies. This demand has driven the transition from CNN-based approaches to Transformer-based architectures. Although Transformers improve global context modeling, their high computational cost limits practical deployment. Recent advances in Mamba enable efficient long-range dependency modeling with reduced complexity, offering a promising alternative that alleviates the efficiency limitations of Transformers while preserving target-level detection performance. However, Mamba is not inherently tailored for IRSTD, as it lacks explicit mechanisms for capturing fine-grained local details and modeling background variations across multiple spatial scales. To address these limitations, we propose MCFNet, an encoder-decoder framework that integrates Mamba to enhance target-level detection performance with moderate computational cost. MCFNet introduces a Detail-Capturable Convolution Block to strengthen local detail perception and a Multi-scale Contextual Mamba Block to improve background modeling across different scales. While the resulting dual-branch design enhances both global semantics and local details, it also introduces challenges in feature fusion. To this end, a Feature Fusion Decoding Module is further proposed to enable effective collaboration between global and local representations. Extensive experiments on multiple public IRSTD benchmark datasets demonstrate that MCFNet consistently outperforms existing methods in both pixel-level and target-level metrics, achieving higher detection accuracy with reduced false alarms. The code of our model is available at: https://github.com/Fihven/MCFNet.