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1. Dense Nested Attention Network for
Infrared Small Target Detection
Abstract
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
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 networ
paper. Specifically, we design a dense nested interactive module (DNIM) to
achieve progressive interaction among high
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
small targets can be well incorporated and fully e
Dense Nested Attention Network for
Infrared Small Target Detection
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
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
ep layers can be maintained. Based on DNIM, we further propose a
cascaded channel and spatial attention module (CSAM) to adaptively
level features. With our DNA-Net, contextual information of
small targets can be well incorporated and fully exploited by repetitive fusion
Dense Nested Attention Network for
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
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
k (DNA-Net) in this
paper. Specifically, we design a dense nested interactive module (DNIM) to
level features. With
the repetitive interaction in DNIM, the information of infrared small targets in
ep layers can be maintained. Based on DNIM, we further propose a
cascaded channel and spatial attention module (CSAM) to adaptively
Net, contextual information of
xploited by repetitive fusion

2. 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 ( ${P}_{d}$ ), false-alarm rate
( ${F}_{a}$ ), and intersection of union ( $IoU$ ).