تقرير
DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR Images
| العنوان: | DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR Images |
|---|---|
| المؤلفون: | Jie, Zhou, Chao, Xiao, Bo, Peng, Zhen, Liu, Li, Liu, Yongxiang, Liu, Xiang, Li |
| المصدر: | IEEE Geoscience and Remote Sensing Letters, vol. 21, pp. 1-5, 2024, Art no. 4007905 |
| سنة النشر: | 2024 |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Electrical Engineering and Systems Science - Signal Processing |
| الوصف: | Aircraft target detection in SAR images is a challenging task due to the discrete scattering points and severe background clutter interference. Currently, methods with convolution-based or transformer-based paradigms cannot adequately address these issues. In this letter, we explore diffusion models for SAR image aircraft target detection for the first time and propose a novel \underline{Diff}usion-based aircraft target \underline{Det}ection network \underline{for} \underline{SAR} images (DiffDet4SAR). Specifically, the proposed DiffDet4SAR yields two main advantages for SAR aircraft target detection: 1) DiffDet4SAR maps the SAR aircraft target detection task to a denoising diffusion process of bounding boxes without heuristic anchor size selection, effectively enabling large variations in aircraft sizes to be accommodated; and 2) the dedicatedly designed Scattering Feature Enhancement (SFE) module further reduces the clutter intensity and enhances the target saliency during inference. Extensive experimental results on the SAR-AIRcraft-1.0 dataset show that the proposed DiffDet4SAR achieves 88.4\% mAP$_{50}$, outperforming the state-of-the-art methods by 6\%. Code is availabel at \href{https://github.com/JoyeZLearning/DiffDet4SAR}. Comment: accepted by IEEE GRSL |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1109/LGRS.2024.3386020 |
| URL الوصول: | http://arxiv.org/abs/2404.03595 |
| رقم الأكسشن: | edsarx.2404.03595 |
| قاعدة البيانات: | arXiv |
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