Recently, infrared small target detection has attracted extensive attention. However, due to the small size and the lack of intrinsic features of infrared small targets, the existing methods generally have the problem of inaccurate edge positioning and the target is easily submerged by the background. Therefore, we propose an innovative gradient-guided learning network (GGL-Net). Specifically, we are the first to explore the introduction of gradient magnitude images into the deep learning-based infrared small target detection method, which is conducive to emphasizing the edge details and alleviating the problem of inaccurate edge positioning of small targets. On this basis, we propose a novel dual-branch feature extraction network that utilizes the proposed gradient supplementary module (GSM) to encode raw gradient information into deeper network layers and embeds attention mechanisms reasonably to enhance feature extraction ability. In addition, we construct a two-way guidance fusion module (TGFM), which fully considers the characteristics of feature maps at different levels. It can facilitate the effective fusion of multi-scale feature maps and extract richer semantic information and detailed information through reasonable two-way guidance. Extensive experiments prove that GGL-Net has achieves state-of-the-art results on the public real NUAA-SIRST dataset and the public synthetic NUDT-SIRST dataset. Our code has been integrated into https://github.com/YuChuang1205/MSDA-Net

翻译：近年来，红外弱小目标检测受到广泛关注。然而，由于红外弱小目标尺寸小且缺乏固有特征，现有方法普遍存在边缘定位不准确以及目标易被背景淹没的问题。为此，我们提出了一种创新的梯度引导学习网络（GGL-Net）。具体而言，我们首次探索将梯度幅值图像引入基于深度学习的红外弱小目标检测方法中，这有助于强调边缘细节并缓解弱小目标边缘定位不准确的问题。在此基础上，我们提出了一种新颖的双分支特征提取网络，利用所提出的梯度补充模块（GSM）将原始梯度信息编码至更深层的网络，并合理嵌入注意力机制以增强特征提取能力。此外，我们构建了一个双向引导融合模块（TGFM），该模块充分考虑了不同层次特征图的特性，能够促进多尺度特征图的有效融合，并通过合理的双向引导提取更丰富的语义信息和细节信息。大量实验证明，GGL-Net在公开的真实数据集NUAA-SIRST和公开的合成数据集NUDT-SIRST上均取得了最先进的结果。我们的代码已集成至https://github.com/YuChuang1205/MSDA-Net。