  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.
