发表机构
School of Electronics and Information, Northwestern Polytechnical University（西北工业大学电子信息学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究针对红外小目标检测难题，提出LCMamNet轻量级跨尺度曼巴网络。通过含CDBR块的编码器强化目标结构，LDCF模块交互跨尺度特征，渐进式解码器恢复细节并抑制背景。实验表明该网络在检测性能、参数和推理延迟上表现出色，有实际应用潜力。
AI 中文摘要
红外小目标检测对低空感知、无人系统预警和安全监控很重要。然而，红外图像中的弱目标通常仅占少数像素，易被云杂波、地面边缘和亮噪声淹没，基于轻量级分割的方法难以在抑制背景干扰时保留局部目标结构。为应对这些挑战，我们提出LCMamNet，一种轻量级跨尺度曼巴网络，它逐步增强局部目标结构，在潜在空间中进行跨尺度上下文交互，并通过背景抑制恢复空间细节。具体来说，具有十字形方向瓶颈残差（CDBR）块的紧凑分层编码器在小计算预算下强化方向敏感目标结构。潜在密集跨尺度融合（LDCF）模块然后通过双向曼巴建模执行密集全级交互，并将交互后的特征重组为稳定的分层语义。最后，渐进式解码器在抑制无关背景纹理的同时选择性地恢复浅层空间细节。在IRSTD-1k、NUAA-SIRST和NUDT-SIRST上的大量实验表明，该网络分别实现了71.25%、79.60%和95.58%的mIoU分数，仅1.175M参数和6.91 GFLOPs。它的平均推理延迟为6.62 ms，在NVIDIA Jetson Orin NX 16G SUPER上的部署结果进一步证明了其在实时边缘推理中的实际潜力。代码和检查点可在该https URL公开获取。
英文摘要
Infrared small target detection (IRSTD) is important for low-altitude perception, unmanned-system warning, and security monitoring. However, weak targets in infrared imagery usually occupy only a few pixels and are easily submerged by cloud clutter, ground edges, and bright noise, making it difficult for lightweight segmentation-based methods to preserve local target structures while suppressing background interference. To address these challenges, we propose LCMamNet, a lightweight cross-scale Mamba network that progressively enhances local target structures, interacts cross-scale context in a latent space, and restores spatial details with background suppression. Specifically, a compact hierarchical encoder with cross-shaped directional bottleneck residual (CDBR) blocks strengthens direction-sensitive target structures under a small computation budget. A latent dense cross-scale fusion (LDCF) module then performs dense all-level interaction through bidirectional Mamba modeling and reorganizes the interacted features into stable hierarchical semantics. Finally, a progressive decoder selectively recovers shallow spatial details while suppressing irrelevant background textures. Extensive experiments on IRSTD-1k, NUAA-SIRST, and NUDT-SIRST show that the proposed network achieves mIoU scores of 71.25\%, 79.60\%, and 95.58\%, respectively, with only 1.175M parameters and 6.91 GFLOPs. It also runs with a mean inference latency of 6.62 ms, and deployment results on an NVIDIA Jetson Orin NX 16G SUPER further demonstrate its practical potential for real-time edge inference. The code and checkpoints are publicly available at https://github.com/Haoyu096/LCMamNet.
