LLCEFA: A Plug-and-Play Lightweight Local Contrast and Edge Fusion Approach for Infrared Small Target Detection Enhancement

The use of local contrast and edge information as prior knowledge is common in hybrid InfraRed Small Target Detection (IRSTD) methods. However, existing methods tightly integrate prior knowledge modules with the backbone network, limiting their flexibility. To address this issue, we propose a Lightweight Local Contrast and Edge Fusion Approach (LLCEFA), featuring a dual-branch architecture with a main branch and an edge branch designed to fully leverage the synergistic advantages of prior knowledge guidance and data-driven learning. Within the main branch, the proposed Local Contrast Attention Fusion (LCAF) module leverages the local contrast prior to reconstruct the input image into more discriminative feature representations. Concurrently, the edge branch employs the developed Adaptive Edge Weight Extraction (AEWE) module to independently derive edge weights for enhancing intermediate-layer features. This design effectively decouples prior knowledge processing from the backbone's architecture, allowing LLCEFA to serve as a plug-and-play enhancer compatible with various backbone methods, achieving a performance boost with introducing only a minimal additional burden. Experimental results validate our LLCEFA's efficacy in enhancing backbone networks, achieving average performance gains of 2.233%,1.826%, and 1.056% in Intersection over Union ($IoU$), normalized Intersection over Union ($nIoU$), and Probability of Detection (${P_{d}}$), respectively, while False-alarm Rate (${F_{a}}$) is reduced by an average of 6.729$\times 10{^{-6}}$across three public datasets. This comes at an average increase of only 0.0009M parameters and 0.0617G Floating-Point Operations (FLOPs) added to the backbone, highlighting its high efficiency and broad applicability.