Dense Nested Attention Network for Infrared Small Target Detection

By Boyang Li, Chao Xiao, Longguang Wang, Yingqian Wang, Zaiping Lin, Miao Li, Wei An, Yulan Guo. IEEE Transactions on Image Processing, 2021

881 citations recorded by the source corpus.

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 to 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 (DNA-Net) 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 repetitive interaction in DNIM, the information of 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 DNA-Net, contextual information of small targets can be well incorporated and fully exploited by repetitive 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 (<inline-formula> <tex-math notation="LaTeX">${P}_{d}$ </tex-math></inline-formula>), false-alarm rate (<inline-formula> <tex-math notation="LaTeX">${F}_{a}$ </tex-math></inline-formula>), and intersection of union (<inline-formula> <tex-math notation="LaTeX">$IoU$ </tex-math></inline-formula>).

Subfields: Aerospace Engineering, Electrical and Electronic Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/19257ee349fa766227fda1e4a0b1f7c12a9638a7

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Citations in the corpus

UIU-Net: U-Net in U-Net for Infrared Small Object Detection (2022, methodology): “However, almost all of the networks in the above work are based on various classiﬁcation backbones with a typical downsampling scheme, such as Resnet-20 used in asymmetric contextual module (ACM) [30] and attentional local contrast network (ALCNet) [31], and Resnet-18/34 used in DNIM [32].”