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Dense Nested Attention Network for Infrared Small Target Detection

📅 August 22, 2022 👤 Boyang Li, Chao Xiao, Longguang Wang et al. 📖 IEEE Transactions on Image Processing 📊 859 citations

🤖 Plain-English Summary

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.

🔑 Key Findings

  • 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.

💡 Why This Matters

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

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📋 Article Details

Category 🤖 Artificial Intelligence
Published Aug 22, 2022
Journal IEEE Transactions on Image Processing
Authors Boyang Li, Chao Xiao, Longguang Wang, Yingqian Wang, Zaiping Lin
DOI 10.1109/tip.2022.3199107
Citations 859
Source OpenAlex

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