Ship Detection Based on Deep Learning

Abstract

China has abundant marine resources and a vast sea area. In military and civilian applications, the detection of targets for marine ships has important research significance. Complex and variable sea conditions make ship detection more difficult. In order to more accurately detect the ship's target, this paper proposes an improved YoloV3 algorithm to realize the end-to-end ship target detection system. Our algorithm achieves high-accuracy fast ship identification by introducing CFE modules, improving loss functions, and augmenting data for small targets. The experimental results show that compared with the traditional machine learning target detection algorithm, our method has greatly improved the detection accuracy and detection rate. It achieves an accuracy of 74.8% and a detection rate of 29.8 frames. The ship detection system of this paper is excellent in detection accuracy and speed.