Dense Nested Attention Network for Infrared Small Target Detection
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 meth...
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| Veröffentlicht in: | IEEE transactions on image processing Jg. 32; S. 1745 - 1758 |
|---|---|
| Hauptverfasser: | , , , , , , , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
United States
IEEE
2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Schlagworte: | |
| ISSN: | 1057-7149, 1941-0042, 1941-0042 |
| Online-Zugang: | Volltext |
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| 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 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>). |
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| AbstractList | 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>). 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 ( P ), false-alarm rate ( F ), and intersection of union ( IoU ). 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 ( Pd ), false-alarm rate ( Fa ), and intersection of union ( IoU ).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 ( Pd ), false-alarm rate ( Fa ), and intersection of union ( IoU ). 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 ([Formula Omitted]), false-alarm rate ([Formula Omitted]), and intersection of union ([Formula Omitted]). |
| Author | Li, Boyang Wang, Yingqian Xiao, Chao Guo, Yulan Wang, Longguang Lin, Zaiping An, Wei Li, Miao |
| Author_xml | – sequence: 1 givenname: Boyang orcidid: 0000-0002-4479-9008 surname: Li fullname: Li, Boyang email: liboyang20@nudt.edu.cn organization: College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China – sequence: 2 givenname: Chao orcidid: 0000-0002-9666-8894 surname: Xiao fullname: Xiao, Chao email: xiaochao12@nudt.edu.cn organization: College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China – sequence: 3 givenname: Longguang orcidid: 0000-0003-0429-0263 surname: Wang fullname: Wang, Longguang email: wanglongguang15@nudt.edu.cn organization: College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China – sequence: 4 givenname: Yingqian orcidid: 0000-0002-9081-6227 surname: Wang fullname: Wang, Yingqian email: wangyingqian16@nudt.edu.cn organization: College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China – sequence: 5 givenname: Zaiping orcidid: 0009-0007-1000-3060 surname: Lin fullname: Lin, Zaiping email: linzaiping@nudt.edu.cn organization: College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China – sequence: 6 givenname: Miao surname: Li fullname: Li, Miao email: lm8866@nudt.edu.cn organization: College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China – sequence: 7 givenname: Wei orcidid: 0000-0001-8319-2105 surname: An fullname: An, Wei email: anwei@nudt.edu.cn organization: College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China – sequence: 8 givenname: Yulan orcidid: 0000-0003-0952-476X surname: Guo fullname: Guo, Yulan email: yulan.guo@nudt.edu.cn organization: College of Electronic Science and Technology, National University of Defense Technology (NUDT), Changsha, China |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/35994532$$D View this record in MEDLINE/PubMed |
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