Effective Sea Clutter Region Extraction Based on Improved YOLOv4 Algorithm for Shore-Based UHF-Band Radar

Sea clutter is the backscattered echo of radar irradiating the sea surface, and this signal data includes not only pure sea clutter, but also often accompanied by many interference signals and noise. Based on the effective sea clutter region in the radar echo image, certain characteristics of the sea clutter can be accurately calculated, which plays a crucial role in the subsequent study of sea clutter and radar performance. The traditional method of using manual detection of effective sea clutter region often requires a lot of time, and with the rapid development of artificial intelligence technology in recent years, it is necessary to establish an effective sea clutter region detection method based on deep learning in Range-Pulse (RP) images. In this paper, based on the sea clutter data collected by shore-based ultra high frequency (UHF) radar, an improved model (H-YOLOv4) based on YOLOv4 network architecture is proposed to solve this problem, which uses the H-swish activation function to improve the performance of the model for effective sea clutter region extraction. In our test set of sea clutter RP images, it is experimentally demonstrated that the AP50 of the model reaches 89.75% with a score threshold of 0.25, which over state-of-the-art method. Compared with the traditional manual detection methods, the model can quickly and accurately extract the effective sea clutter region and maintain a good detection speed.