MSMA-Net: An Infrared Small Target Detection Network by Multi-scale Super-resolution Enhancement and Multi-level Attention Fusion

Infrared small target detection plays a crucial role in various domains like early warning, national defense, and monitoring. Although existing detection methods have achieved some good results, they only rely on the original size and information of small targets for detection. And faced with challenges such as the small size and obscure feature information of small targets. To overcome these limitations, this paper introduces a coarse-to-fine detection network named MSMA-Net. This network initially determines the rough location of targets through a coarse preliminary screening, aiming to reduce false alarms and improve computational efficiency. Simultaneously, to improve the discriminability of the features and enhance the spatial details and resolution of the targets, the network utilizes multi-scale super-resolution to transform low-resolution feature maps into high-resolution representations, gradually refining and strengthening the feature representation. Finally, the network employs a multi-level feature fusion attention mechanism to facilitate effective information transmission and fusion in multi-scale and multi-level feature representations. This attention mechanism enhances the accuracy as well as robustness of object detection, ultimately obtaining accurate detection results. Extensive experimental results demonstrate that compared to existing detection methods, our approach can effectively suppress false alarms and get better performance even when the target has a small size and obscure feature information.