Memory-Augmented Differential Network for Infrared Small Target Detection

Abstract

Traditional U-Net-based methods in infrared small target detection (IRSTD) have demonstrated good performance. However, they often struggle with challenges such as blurred contour and strong interference in complex backgrounds. To overcome these issues, we propose a memory-augmented differential network (MAD-Net), which integrates two key modules: the adaptive differential convolution module (AdaDCM) and the memory-augmented attention module (MemA2M). AdaDCM leverages multiple differential convolutions to capture detailed edge information, with an adaptive fusion mechanism to weight and aggregate these features. In the deeper layers, by introducing the dataset-level representations through a learnable memory bank (LMB), MemA2M can enhance current features and effectively mitigate background interference. Extensive experiments on four public IRSTD datasets demonstrate that MAD-Net outperforms state-of-the-art methods, showcasing its superior capability in handling complex scenarios. The code is available at: https://github.com/joan2joan/MAD-Net .