LIR-Mamba: consolidating YOLO and selective SSM with global-local scanning for robust infrared small target detection under laser interference.

Detecting dim and small infrared targets under high-energy laser jamming is a critical prerequisite for modern electro-optical defense systems. However, intense glare and speckle noise severely disrupt spatial distributions, making robust detection on resource-constrained edge platforms highly challenging. Existing convolutional neural networks lack the global receptive field required to suppress large-scale jamming, while Vision Transformers incur prohibitive quadratic computational costs. Although emerging State Space Models (SSMs) offer linear complexity, their standard 1D scanning strategies inherently disrupt the 2D local spatial continuity of infrared images, causing fine-grained features of tiny targets to dissipate in strong background noise. To resolve these critical bottlenecks, we propose LIR-Mamba, a novel lightweight detection framework that synergizes the real-time efficiency of YOLO with the global modeling prowess of selective SSMs. At its core, we introduce an innovative Global-Local Selective Scanning 2D (GLSS2D) mechanism. By enforcing local scanning paths within spatial windows alongside global traversal, GLSS2D effectively bridges the gap between 1D sequence modeling and 2D spatial locality, firmly preventing the dispersion of local hot-spot features. Extensive experiments demonstrate that LIR-Mamba achieves a superior mAP@50 of 96.3% at 66 frames per second, significantly outperforming mainstream detectors such as YOLOv11 and RT-DETR in both accuracy and anti-jamming robustness. The proposed method provides competitive performance for real-time target detection in complex electro-optical countermeasure environments.