Constructing a high-performance target detector under the background of sea clutter is always necessary and important. In this work, we propose a RepVGGA0-CWT detector, where RepVGG is a residual network that gains a high detection accuracy. Different from traditional residual networks, RepVGG keeps an acceptable calculation speed. Giving consideration to both accuracy and speed, the RepVGGA0 is selected among all the variants of RepVGG. Also, continuous wavelet transform (CWT) is employed to extract the radar echoes' time-frequency feature effectively. In the tests, other networks (ResNet50, ResNet18 and AlexNet) and feature extraction methods (short-time Fourier transform (STFT), CWT) are combined to build detectors for comparison. The result of different datasets shows that the RepVGGA0-CWT detector performs better than those detectors in terms of low controllable false alarm rate, high training speed, high inference speed and low memory usage. This RepVGGA0-CWT detector is hardware-friendly and can be applied in real-time scenes for its high inference speed in detection.

翻译：在海杂波背景下构建高性能目标探测器始终具有必要性和重要性。本文提出一种RepVGGA0-CWT检测器，其中RepVGG是一种具有高检测精度的残差网络。与传统残差网络不同，RepVGG保持了可接受的计算速度。兼顾精度与速度，我们选取了RepVGG所有变体中的RepVGGA0。同时，采用连续小波变换有效提取雷达回波的时频特征。实验中，将其他网络（ResNet50、ResNet18、AlexNet）与特征提取方法（短时傅里叶变换、连续小波变换）组合构建检测器进行对比。多组数据集结果表明，RepVGGA0-CWT检测器在低可控虚警率、高训练速度、高推理速度和低内存占用方面均优于对比检测器。该RepVGGA0-CWT检测器具备硬件友好性，其高推理速度使其可应用于实时检测场景。