Ship detection from Synthetic Aperture Radar (SAR) imagery is attracting increasing attention due to its great value in ocean. However, existing most studies are frequently improving detection accuracy at the expense of detection speed. Thus, to solve this problem, this paper proposes HyperLi-Net for high-accurate and high-speed SAR ship detection. We propose five external modules to achieve high-accuracy, i.e., Multi-Receptive-Field Module (MRF-Module), Dilated Convolution Module (DC-Module), Channel and Spatial Attention Module (CSA-Module), Feature Fusion Module (FF-Module) and Feature Pyramid Module (FP-Module). We also adopt five internal mechanisms to achieve high-speed, i.e., Region-Free Model (RF-Model), Small Kernel (S-Kernel), Narrow Channel (N-Channel), Separable Convolution (Separa-Conv) and Batch Normalization Fusion (BN-Fusion). Experimental results on the SAR Ship Detection Dataset (SSDD), Gaofen-SSDD and Sentinel-SSDD show that HyperLi-Net’s accuracy and speed are both superior to the other nine state-of-the-art methods. Moreover, the satisfactory detection results on two Sentinel-1 SAR images can reveal HyperLi-Net’s good migration capability. HyperLi-Net is build from scratch with fewer parameters, lower computation costs and lighter model that can be efficiently trained on CPUs and is helpful for future hardware transplantation, e.g. FPGAs, DSPs, etc.

由于合成孔径雷达（SAR）图像在海洋中的巨大价值，因此其在船舶探测中的应用日益受到关注。然而，现有的大多数研究经常以牺牲检测速度为代价来提高检测精度。因此，为解决这个问题，本文提出了HyperLi-Net用于高精度和高速SAR船舶检测。我们提出了五个实现高精度的外部模块，即多接收场模块（MRF-Module），膨胀卷积模块（DC-Module），通道和空间注意模块（CSA-Module），特征融合模块（FF） -Module）和功能金字塔模块（FP-Module）。我们还采用五种内部机制来实现高速，即无区域模型（RF-Model），小内核（S-Kernel），窄通道（N-Channel），可分卷积（Separa-Conv）和批处理归一化融合（BN-融合）。在SAR船舶检测数据集（SSDD），Gaofen-SSDD和Sentinel-SSDD上的实验结果表明，HyperLi-Net的准确性和速度均优于其他九种最新方法。此外，在两个Sentinel-1 SAR图像上的令人满意的检测结果可以显示HyperLi-Net的良好迁移能力。HyperLi-Net是从头开始构建的，具有更少的参数，更低的计算成本和更轻便的模型，可以在CPU上有效地对其进行训练，并且有助于将来的硬件移植，例如FPGA，DSP等。