Pretreatment Method of Deep Learning Method for Target Detection in Sea Clutter

Abstract

Sea target detection is widely used in military and civilian fields. Because of the space-time non-stationary characteristics of high resolution radar sea clutter, traditional target detection methods have many limitations and are limited by the use of scenarios. In recent years, with the progress of deep learning in image classification tasks, a series of target detection methods based on deep learning have emerged. By applying these methods to target detection in radar sea clutter, high accuracy and good generalization can be obtained. However, there are many new problems when these target detection methods, mostly based on computer vision, are introduced to target detection in radar sea clutter due to different data forms and detection standards. This paper mainly discusses the effects of different pretreatment modes of target detection in sea clutter using classification target detection framework on training speed and detection accuracy.