Abstract：The detection of marine targets is an important part of radar signal processing, and has important application value in military and civilian fields. For the radar signal processing of marine targets, the presence of sea clutter has a great influence on the performance of the detection algorithm. Traditional radar signal processing methods are mostly based on statistical theory, and the detection performance under complex environmental conditions and diverse target characteristics may decrease significantly. In recent years, deep learning technology has developed rapidly, providing technical support for reliable target detection methods. This paper summarizes the development of target detection algorithms and deep learning methods in recent years. Starting from the data structure and dimensions of radar signals, using deep learning theory, the paper proposes a multi ?channel fusion flowchart based on two ?dimensional images, three ?dimensional frames processing, and multi ?dimension information fusion intelligent processing framework. Taking the detection of marine targets in navigation radar images as an example, a Precise ROI ?Faster R?CNN radar image detection algorithm is proposed. It is compared with the classic method of target detection through the training and testing of the constructed navigation radar dataset. It is indicated that the proposed method is an effective technical approach with higher detection accuracy and better generalization ability compared with Classical False Alarm Rate(CFAR) and Faster R? CNN methods. It can be an effective solution for intelligent navigation and target detection of marine radars.