DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR Images
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917642254680064 |
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| author | Jie, Zhou Chao, Xiao Bo, Peng Zhen, Liu Li, Liu Yongxiang, Liu Xiang, Li |
| author_facet | Jie, Zhou Chao, Xiao Bo, Peng Zhen, Liu Li, Liu Yongxiang, Liu Xiang, Li |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_03595 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR Images Jie, Zhou Chao, Xiao Bo, Peng Zhen, Liu Li, Liu Yongxiang, Liu Xiang, Li Image and Video Processing Signal Processing 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}. |
| title | DiffDet4SAR: Diffusion-based Aircraft Target Detection Network for SAR Images |
| topic | Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2404.03595 |