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Xin Wu, Danfeng Hong, Jocelyn Chanussot
Learning-based infrared small object detection methods currently rely heavily on the classification backbone network. This tends to result in tiny object loss and feature distinguishability limitations as the network depth increases. Furthermore, small objects in infrared images are frequently emerged bright and dark, posing severe demands for obtaining precise object contrast information. For this reason, we in this paper propose a simple and effective "U-Net in U-Net" framework, UIU-Net for short, and detect small objects in infrared images. As the name suggests, UIU-Net embeds a tiny U-Net into a larger U-Net backbone, enabling the multi-level and multi-scale representation learning of objects. Moreover, UIU-Net can be trained from scratch, and the learned features can enhance global and local contrast information effectively. More specifically, the UIU-Net model is divided into two modules: the resolution-maintenance deep supervision (RM-DS) module and the interactive-cross attention (IC-A) module. RM-DS integrates Residual U-blocks into a deep supervision network to generate deep multi-scale resolution-maintenance features while learning global context information. Further, IC-A encodes the local context information between the low-level details and high-level semantic features. Extensive experiments conducted on two infrared single-frame image datasets, i.e., SIRST and Synthetic datasets, show the effectiveness and superiority of the proposed UIU-Net in comparison with several state-of-the-art infrared small object detection methods. The proposed UIU-Net also produces powerful generalization performance for video sequence infrared small object datasets, e.g., ATR ground/air video sequence dataset. The codes of this work are available openly at https://github.com/danfenghong/IEEE.
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Wu, X., Hong, D., & Chanussot, J. (2022). UIU-Net: U-Net in U-Net for Infrared Small Object Detection. IEEE Transactions on Image Processing, 32, 364–376. https://doi.org/10.1109/tip.2022.3228497
Wu, Xin, et al. "UIU-Net: U-Net in U-Net for Infrared Small Object Detection." IEEE Transactions on Image Processing, vol. 32, 2022, pp. 364–376, https://doi.org/10.1109/tip.2022.3228497.
Wu, Xin, Danfeng Hong, and Jocelyn Chanussot. "UIU-Net: U-Net in U-Net for Infrared Small Object Detection." IEEE Transactions on Image Processing 32 (2022): 364–376. https://doi.org/10.1109/tip.2022.3228497.
Wu X., Hong D., Chanussot J. UIU-Net: U-Net in U-Net for Infrared Small Object Detection // IEEE Transactions on Image Processing. — 2022. — Vol. 32. — P. 364–376. — URL: https://doi.org/10.1109/tip.2022.3228497
@article{wu2022uiunet,
title = {{UIU-Net: U-Net in U-Net for Infrared Small Object Detection}},
author = {Xin Wu and Danfeng Hong and Jocelyn Chanussot},
journal = {IEEE Transactions on Image Processing},
year = {2022},
volume = {32},
pages = {364--376},
doi = {10.1109/tip.2022.3228497},
url = {https://doi.org/10.1109/tip.2022.3228497}
}TY - JOUR AU - Wu, Xin AU - Hong, Danfeng AU - Chanussot, Jocelyn TI - UIU-Net: U-Net in U-Net for Infrared Small Object Detection JO - IEEE Transactions on Image Processing VL - 32 SP - 364 EP - 376 PY - 2022 DA - 2022/12/15 DO - 10.1109/tip.2022.3228497 UR - https://doi.org/10.1109/tip.2022.3228497 ER -
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