Dense Nested Attention Network for Infrared Small Target Detection

Single-frame infrared small target (SIRST) detection aims at separating small targets from clutter backgrounds. Experiments on both public and our self-developed datasets demonstrate the effectiveness of our method.

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• 1 With the advances of deep learning, CNN-based methods have yielded promising results in generic object detection due to their powerful modeling capability.
• 2 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.
• 3 To handle this problem, we propose a dense nested attention network (DNA-Net) in this paper.

This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.

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