Light but Mighty: When Lightweight Meets Stable Detection in Infrared Small Target Detection

With the development of deep learning, infrared small target detection methods have yielded promising results benefiting from the powerful feature extraction capability of deep neural networks. However, these methods with large numbers of parameters are often impractical due to hardware limitations, while existing lightweight models tend to be unstable as they struggle to effectively capture small targets. To cope with these challenges, we propose a novel light but mighty network Limi-Net , which maintains lightweight while having a stable ability to capture small targets. First, since the targets are rare in infrared images, we propose an Infrared Target Simulator that generates pseudo targets for data augmentation, helping the model to better learn and recognize small targets. Then a Lightweight Stable Encoder is designed to guarantee reliable feature extraction from diverse receptive fields to improve the discrimination of small targets and reduce memory consumption. In addition, we introduce a Coarse-to-fine Hybrid Upsampling Decoder that combines a dual upsampling fusion method and a coarse to fine alignment mechanism to integrate multi-scale features while preserving critical information. Extensive experiments demonstrate that Limi-Net achieves state-of-the-art SOTA performance while maintaining a lightweight architecture, making it well-suited for practical deployment. Our code is available at https: github.com Arrosw LimiNet.