Embedded Deep Learning for Ship Detection and Recognition

Hongwei Zhao, Weishan Zhang, Haoyun Sun and Bing Xue
Additional contact information
Hongwei Zhao: College of Computer and Communication Engineering, China University of Petroleum (UPC), Qingdao 266580, China
Weishan Zhang: College of Computer and Communication Engineering, China University of Petroleum (UPC), Qingdao 266580, China
Haoyun Sun: College of Computer and Communication Engineering, China University of Petroleum (UPC), Qingdao 266580, China
Bing Xue: College of Computer and Communication Engineering, China University of Petroleum (UPC), Qingdao 266580, China

Future Internet, 2019, vol. 11, issue 2, 1-12

Abstract: Ship detection and recognition are important for smart monitoring of ships in order to manage port resources effectively. However, this is challenging due to complex ship profiles, ship background, object occlusion, variations of weather and light conditions, and other issues. It is also expensive to transmit monitoring video in a whole, especially if the port is not in a rural area. In this paper, we propose an on-site processing approach, which is called Embedded Ship Detection and Recognition using Deep Learning (ESDR-DL). In ESDR-DL, the video stream is processed using embedded devices, and we design a two-stage neural network named DCNet, which is composed of a DNet for ship detection and a CNet for ship recognition, running on embedded devices. We have extensively evaluated ESDR-DL, including performance of accuracy and efficiency. The ESDR-DL is deployed at the Dongying port of China, which has been running for over a year and demonstrates that it can work reliably for practical usage.

Keywords: ship identification; fully convolutional network; embedded deep learning; scalability (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (3)

Downloads: (external link)
https://www.mdpi.com/1999-5903/11/2/53/pdf (application/pdf)
https://www.mdpi.com/1999-5903/11/2/53/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jftint:v:11:y:2019:i:2:p:53-:d:207920

Access Statistics for this article

Future Internet is currently edited by Ms. Grace You

More articles in Future Internet from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().