The data-driven method for infrared small target detection (IRSTD) has achieved promising results. However, due to the small scale of infrared small target datasets and the limited number of pixels occupied by the targets themselves, it is a challenging task for deep learning methods to directly learn from these samples. Utilizing human expert knowledge to assist deep learning methods in better learning is worthy of exploration. To effectively guide the model to focus on targets' spatial features, this paper proposes the Local Contrast Attention Enhanced infrared small target detection Network (LCAE-Net), combining prior knowledge with data-driven deep learning methods. LCAE-Net is a U-shaped neural network model which consists of two developed modules: a Local Contrast Enhancement (LCE) module and a Channel Attention Enhancement (CAE) module. The LCE module takes advantages of prior knowledge, leveraging handcrafted convolution operator to acquire Local Contrast Attention (LCA), which could realize background suppression while enhance the potential target region, thus guiding the neural network to pay more attention to potential infrared small targets' location information. To effectively utilize the response information throughout downsampling progresses, the CAE module is proposed to achieve the information fusion among feature maps' different channels. Experimental results indicate that our LCAE-Net outperforms existing state-of-the-art methods on the three public datasets NUDT-SIRST, NUAA-SIRST, and IRSTD-1K, and its detection speed could reach up to 70 fps. Meanwhile, our model has a parameter count and Floating-Point Operations (FLOPs) of 1.945M and 4.862G respectively, which is suitable for deployment on edge devices.

翻译：基于数据驱动的红外小目标检测方法已取得显著成果。然而，由于红外小目标数据集的规模较小且目标本身所占像素有限，深度学习方法直接从此类样本中学习仍是一项具有挑战性的任务。利用人类专家知识辅助深度学习方法进行更有效的学习值得深入探索。为有效引导模型关注目标的空间特征，本文提出结合先验知识与数据驱动深度学习的局部对比度注意力增强红外小目标检测网络（LCAE-Net）。LCAE-Net是一种U型神经网络模型，包含两个创新模块：局部对比度增强模块与通道注意力增强模块。局部对比度增强模块借助先验知识，利用手工设计的卷积算子获取局部对比度注意力，在抑制背景的同时增强潜在目标区域，从而引导神经网络更关注潜在红外小目标的位置信息。为有效利用下采样过程中的响应信息，通道注意力增强模块实现了特征图不同通道间的信息融合。实验结果表明，我们的LCAE-Net在NUDT-SIRST、NUAA-SIRST和IRSTD-1K三个公开数据集上均优于现有先进方法，检测速度可达70帧/秒。同时，模型参数量与浮点运算量分别为1.945M和4.862G，适用于边缘设备部署。