Small targets are often submerged in the cluttered backgrounds of infrared images. In this paper, we propose an iterative feedback UNet++ for infrared small target detection, dubbed ifUNet++. Unlike most of existing methods, ifU-Net++ enables to concentrate on small targets while weakening the interference of clutter backgrounds. ifUNet++ contains two parts: a simplified UNet++ and an iterative feedback strategy. We reduce the unnecessary nodes of UNet++ and have the simplified UNet++ as our backbone network, avoiding the loss of infrared small targets. Based on the simplified network, we search the infrared small targets in an iterative feedback manner, avoiding the interference of cluttered backgrounds. Besides, to optimize the iterative results, we propose Contextual Multiple Attention (CMA) to enhance the features in each iteration. Experimental results exhibit the clear promotion of ifUNet++ over eight state-of-the-art methods, in terms of noise-robustness and detection accuracy.
|Title of host publication
|ICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
|Institute of Electrical and Electronics Engineers Inc.
|Published - 2023
|48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece
Duration: 4 Jun 2023 → 10 Jun 2023
|ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
|48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
|4/06/23 → 10/06/23
Bibliographical notePublisher Copyright:
© 2023 IEEE.
- Infrared small target detection
- Iterative feedback