Computer Vision and Deep Learning in Ship Target Detection

Abstract

With the rapid development of the Marine economy in recent years and the increasing frequency of maritime activities, the criticality of ship target detection in many aspects has become increasingly evident, such as port management, maritime traffic safety, and military defense. By integrating computer vision and deep learning technologies, It provides an efficient and accurate solution for ship target detection. This article will first discuss the significance of ship target detection, and then analyze the technical challenges it faces. These include the interference brought by the complex Marine environment, the difficulties in detecting small targets, the need to handle multi-target occlusion and dense scenes, as well as the need to balance real-time performance and computing power limitations. Next, we will provide a detailed introduction to the key technical paths of deep learning in ship target detection. This includes data-driven methods, the development and changes of convolutional neural network architectures, attention mechanisms and feature fusion strategies, as well as lightweight network design. We will also discuss the application of computer vision and deep learning in some systems and fields. Such as port vessel surveillance systems, maritime traffic management systems, military target identification, and Marine environment monitoring, etc., and it will summarize the future development trends and all its potential.