Marine Object Recognition Based on Deep Learning

DOI

10.2991/cnci-19.2019.43How to use a DOI?

Keywords

Unmanned surface vessel, Deep learning, Single Shot MultiBox Detector, Marine object detection.

Abstract

In the research of unmanned surface vessel(USV), accurately perceiving the environment around the USV and recognizing the obstacles in real time are the major difficulties. The existing methods based on lidar or unmanned air vehicle have got good performance, but time and money costs are not what we can afford. After analyzing the difficulties existed in the obstacle avoidance test for USV, we propose a new method called marine object detection based on Single Shot MultiBox Detector(SSD). It solves these difficulties well, and the time and money costs are acceptable to us. After modifying and optimizing the SSD model, its average precision is 93.5% and its time cost is 45ms per image(1280*760), which means that it has much better performance than any existing method. The experimental results show that the method can detect object in real time and have great precision, which ensures the safety of USV during the navigation.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Bo Shi
AU  - Hao Zhou
PY  - 2019/05
DA  - 2019/05
TI  - Marine Object Recognition Based on Deep Learning
BT  - Proceedings of the 2019 International Conference on Computer, Network, Communication and Information Systems (CNCI 2019)
PB  - Atlantis Press
SP  - 290
EP  - 298
SN  - 2352-538X
UR  - https://doi.org/10.2991/cnci-19.2019.43
DO  - 10.2991/cnci-19.2019.43
ID  - Shi2019/05
ER  -