Marine ship detection on the point cloud dataset of autonomous navigation ship models

被引：0
|
作者：
He Y. [1 ,2 ,3 ]
Xia G. [1 ,2 ,3 ]
Feng H. [1 ,2 ,3 ]
Xiang J. [1 ,2 ,3 ]
Hu N. [1 ,2 ,3 ]
机构：
[1] College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin
[2] Heilongjiang Provincial Key Laboratory of Environment Intelligent Perception, Harbin
[3] Key Laboratory of Intelligent Technology, Application of Marine Equipment (Harbin Engineering University), Ministry of Education, Harbin
来源：
Harbin Gongcheng Daxue Xuebao/Journal of Harbin Engineering University | 2022年 / 43卷 / 08期
关键词：
computer vision; dataset; deep learning; LiDAR; marine environment; object detection; point cloud; ship;
D O I：
10.11990/jheu.202204007
中图分类号：
学科分类号：
摘要：
In order to start the research work of LiDAR maritime object detection, a ship point cloud dataset was established by equivalently collecting the marine point cloud data using autonomous navigation ship models and LI-DAR. Using the deep learning method, a point-structured lightweight object detection network LASSD was proposed for ship point cloud object detection, and the network pruning method improved the speed and reduced the required computing resources. A local attention module for high-level point cloud features based on candidate targets was proposed to compensate for the accuracy loss caused by network pruning. Experiments show that the LASSD network in this paper uses only 5. 3×106 parameters to achieve 79. 42% accuracy in the ship dataset, and single scene detection only takes 13. 5 ms. Detection accuracy and operation speed can provide valid results in practical applications. © 2022 Editorial Board of Journal of Harbin Engineering. All rights reserved.
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页码：1156 / 1162and1168
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