Ship feature recognition methods for deep learning in complex marine environments

Abstract

With the advancement of edge computing, the computing power that was originally located in the center is deployed closer to the terminal, which directly accelerates the iteration speed of the "sensing-communication-decision-feedback" chain in the complex marine environments, including ship avoidance. The increase in sensor equipment, such as cameras, have also accelerated the speed of ship identification technology based on feature detection in the maritime field. Based on the SSD framework, this article proposes a deep learning model called DP-SSD. By adjusting the size of the detection frame, different feature parameters can be detected. Through actual data learning and testing, it is compatible with Faster RCNN, SSD and other classic algorithms. It was found that the proposed method provided high-quality results in terms of the calculation time, the processed frame rate, and the recognition accuracy. As an important part of future smart ships, this method has theoretical value and an influence on engineering.