稠密嵌套注意力网络在红外小目标检测中的应用
Dense Nested Attention Network for Infrared Small Target Detection
摘要：
单帧红外小目标检测的目的是将小目标从杂波背景中分离出来。随着深度学习的发展，基于CNN的方法由于其强大的建模能力，在通用目标检测中取得了很好的结果。然而，现有的基于CNN的方法不能直接应用于红外小目标，因为在其网络中的池层可能会导致深层目标的丢失。为了解决这个问题，本文提出了一种密集嵌套注意力网络（DNANet）。具体来说，我们设计了一个密集嵌套交互模块（DNIM），以实现高级和低级功能之间的渐进式交互。利用DNIM中的重复作用，可以保持深层红外小目标。在DNIM的基础上，我们进一步提出了一个级联的通道和空间注意力模块（CSAM），以自适应地增强多级特征。利用我们的DNANet，小目标的上下文信息可以很好地结合起来，并通过反复的融合和增强来充分利用。此外，我们开发了一个红外小目标数据集（即NUDT-SILLE），并提出了一套评估指标进行全面的性能评估。在公开数据集和我们自己开发的数据集上的实验证明了该方法的有效性。与其他最先进的方法相比，我们的方法在检测概率（Pd），误报率（Fa）和交集（IoU）方面取得了更好的性能。
英文摘要：
Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds. With the advances of deep learning, CNN-based methods have yielded promising results in generic object detection due to their powerful modeling capability. However, existing CNN-based methods cannot be directly applied for infrared small targets since pooling layers in their networks could lead to the loss of targets in deep layers. To handle this problem, we propose a dense nested attention network (DNANet) in this paper. Specifically, we design a dense nested interactive module (DNIM) to achieve progressive interaction among high-level and low-level features. With the repeated interaction in DNIM, infrared small targets in deep layers can be maintained. Based on DNIM, we further propose a cascaded channel and spatial attention module (CSAM) to adaptively enhance multi-level features. With our DNANet, contextual information of small targets can be well incorporated and fully exploited by repeated fusion and enhancement. Moreover, we develop an infrared small target dataset (namely, NUDT-SIRST) and propose a set of evaluation metrics to conduct comprehensive performance evaluation. Experiments on both public and our self-developed datasets demonstrate the effectiveness of our method. Compared to other state-of-the-art methods, our method achieves better performance in terms of probability of detection (Pd), false-alarm rate (Fa), and intersection of union (IoU).
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