Open AccessA Water Surface Moving Target Detection Based on Information Fusion Using Deep Learning
Author(s) -
Wei Zhang,
Chengxiang Yang,
Feng Jiang,
Xian-Zhong Gao,
Kai Yang
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1606/1/012020
Subject(s) - point cloud , remote sensing , computer science , obstacle , artificial intelligence , computer vision , unmanned surface vehicle , deep learning , geography , marine engineering , engineering , archaeology
An unmanned surface vehicle (usually referred to as USV) is an unmanned surface ship that has been developed in recent years. Surface unmanned boats can be used for a variety of civil and military missions, such as marine environmental monitoring, personnel search, anti-mine mines; island map mapping, offshore facility maintenance, and hydrographic surveys. Therefore, the research on unmanned surface vehicles has important theoretical significance and practical application value. The optical image information collected by the camera and the point cloud information collected by the laser radar are used for information fusion, and the optical image is processed by the deep neural network to obtain the target type, the detection confidence and the target position. At the same time, the point cloud information of the lidar image is used to obtain the distance and orientation information of the target. Finally, the two information is combined to obtain the type of surface target or obstacle, the confidence of the detection, and the distance and bearing information from the unmanned surface vehicles.