MEETNet: Morphology-Edge Enhanced Triple-Cascaded Network for Infrared Small Target Detection

Infrared small target detection is focused on accurately identifying tiny targets with low signal-to-noise ratio against complex backgrounds, representing a critical challenge in the field of infrared image processing. Existing approaches frequently fail to retain small target information during global semantic extraction and struggle with preserving detailed features and achieving effective feature fusion. To address these limitations, this paper proposes a Morphology-Edge Enhanced Triple-Cascaded Network (MEETNet) for infrared small target detection. The network employs a triple-cascaded architecture that maintains high resolution and enhances information interaction between different stages, facilitating effective multi-level feature fusion while safeguarding deep small-target characteristics. MEETNet integrates an Edge-Detail Enhanced Module (EDEM) and a Detail-Aware Multi-Scale Fusion Module (DMSFM). These modules introduce edge-detail enhanced features that amalgamate contrast and edge information, thereby amplifying target saliency and improving edge representation. Specifically, EDEM augments target contrast and edge structures by integrating edge-detail-enhanced features with shallow details. This integration improves the discriminability capacity of shallow features for detecting small targets. Moreover, DMSFM implements a multi-receptive field mechanism to merge target details with deep semantic insights, enabling the capture of more distinctive global contextual features. Experimental evaluations conducted using two public datasets–NUAA-SIRST and NUDT-SIRST–demonstrate that the proposed MEETNet surpasses existing state-of-the-art methods for infrared small target detection in terms of detection accuracy.