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Boyang Li, Chao Xiao, Longguang Wang, Yingqian Wang, Zaiping Lin, Miao Li, Wei An, Yulan Guo
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 (${P}_{d}$), false-alarm rate (${F}_{a}$), and intersection of union ($IoU$).
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Li, B., Xiao, C., Wang, L., Wang, Y., Lin, Z., Li, M., An, W., & Guo, Y. (2022). Dense Nested Attention Network for Infrared Small Target Detection. IEEE Transactions on Image Processing, 32, 1745–1758. https://doi.org/10.1109/tip.2022.3199107
Li, Boyang, et al. "Dense Nested Attention Network for Infrared Small Target Detection." IEEE Transactions on Image Processing, vol. 32, 2022, pp. 1745–1758, https://doi.org/10.1109/tip.2022.3199107.
Li, Boyang, Chao Xiao, Longguang Wang, Yingqian Wang, Zaiping Lin, Miao Li, Wei An, and Yulan Guo. "Dense Nested Attention Network for Infrared Small Target Detection." IEEE Transactions on Image Processing 32 (2022): 1745–1758. https://doi.org/10.1109/tip.2022.3199107.
Li B., Xiao C., Wang L. [и др.] Dense Nested Attention Network for Infrared Small Target Detection // IEEE Transactions on Image Processing. — 2022. — Vol. 32. — P. 1745–1758. — URL: https://doi.org/10.1109/tip.2022.3199107
@article{li2022dense,
title = {{Dense Nested Attention Network for Infrared Small Target Detection}},
author = {Boyang Li and Chao Xiao and Longguang Wang and Yingqian Wang and Zaiping Lin and Miao Li and Wei An and Yulan Guo},
journal = {IEEE Transactions on Image Processing},
year = {2022},
volume = {32},
pages = {1745--1758},
doi = {10.1109/tip.2022.3199107},
url = {https://doi.org/10.1109/tip.2022.3199107}
}TY - JOUR AU - Li, Boyang AU - Xiao, Chao AU - Wang, Longguang AU - Wang, Yingqian AU - Lin, Zaiping AU - Li, Miao AU - An, Wei AU - Guo, Yulan TI - Dense Nested Attention Network for Infrared Small Target Detection JO - IEEE Transactions on Image Processing VL - 32 SP - 1745 EP - 1758 PY - 2022 DA - 2022/08/22 DO - 10.1109/tip.2022.3199107 UR - https://doi.org/10.1109/tip.2022.3199107 ER -
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