Dense Nested Attention Network for Infrared Small Target Detection.pdf
- 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 ( $IoU$ ).