H2Det: A High-Speed and High-Accurate Ship Detector in SAR Images

Synthetic aperture radar (SAR) sensor is a vital platform for ship detection whose accuracy and speed are usually difficult to balance. An urgent problem to be solved is how to achieve high-speed detection while maintaining high-accurate. To address this problem, we propose a high-speed and high-accurate detector (H2Det) in SAR images. For one thing, we adopt fewer convolutional layers, CSP module, and rectangle filling to ensure model high-speed. For another, we propose spatial pyramid pooling, bottom-up path augmentation, and mosaic data augmentation to ensure model high-accurate. To establish an optimal H2Det, we conduct comparative studies on SAR ship detection dataset (SSDD). Moreover, we verify the effectiveness of these modules mentioned above through ablation studies. The experimental results on SSDD demonstrate that both accuracy and speed of the proposed method outperform other state-of-the-art methods and references. In addition, the strong migration ability of the proposed H2Det is shown on high-resolution SAR images dataset.