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Lightweight Mamba Network Excels at Infrared Small Target Detection

Researchers have developed LCMamNet, a novel lightweight network designed for infrared small target detection. This network utilizes a Mamba architecture to progressively enhance local target structures and fuse cross-scale contextual information. Experiments on several datasets show LCMamNet achieves high accuracy with significantly fewer parameters and lower computational cost compared to existing methods. Its efficient performance also makes it suitable for real-time deployment on edge devices like the NVIDIA Jetson Orin NX. AI

IMPACT This research offers a more efficient approach to infrared small target detection, potentially enabling real-time applications on edge devices.

RANK_REASON The cluster describes a new research paper detailing a novel neural network architecture for a specific computer vision task.

Read on Hugging Face Daily Papers →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Lightweight Mamba Network Excels at Infrared Small Target Detection

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The cluster describes a new research paper detailing a novel neural network architecture for a specific computer vision task.
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COVERAGE [2]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    LCMamNet: A Lightweight Cross-scale Mamba Network for Infrared Small Target Detection

    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…

  2. arXiv cs.CV TIER_1 English(EN) · Yuhao Fan, Le Hui, Yuchao Dai ·

    LCMamNet: A Lightweight Cross-scale Mamba Network for Infrared Small Target Detection

    arXiv:2607.24184v1 Announce Type: new Abstract: 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 …