DiffDet4SAR: Diffusion-Based Aircraft Target Detection Network for SAR Images

Jie Zhou, Chao Xiao, Bo Peng, Zhen Liu, Li Liu, Yongxiang Liu, Xiang Li

Abstract: Aircraft target detection in synthetic aperture radar (SAR) images is a challenging task due to the discrete scattering points and severe background clutter interference. Currently, methods with convolution- 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 Diffusion-based aircraft target Detection network for 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 a 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% mAP50, outperforming the state-of-the-art methods by 6%. The code is available at https://github.com/JoyeZLearning/DiffDet4SAR .