Dense Nested Attention Network for Infrared Small Target Detection
- URL: http://arxiv.org/abs/2106.00487v1
- Date: Tue, 1 Jun 2021 13:45:35 GMT
- Title: Dense Nested Attention Network for Infrared Small Target Detection
- Authors: Boyang Li, Chao Xiao, Longguang Wang, Yingqian Wang, Zaiping Lin, Miao
Li, Wei An, Yulan Guo
- Abstract summary: Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds.
Existing CNN-based methods cannot be directly applied for infrared small targets.
We propose a dense nested attention network (DNANet) in this paper.
- Score: 36.654692765557726
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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 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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