文章基本信息

标题：Embedded Deep Learning for Ship Detection and Recognition
本地全文：下载
作者：Hongwei Zhao ; Weishan Zhang ; Haoyun Sun 等
期刊名称：Future Internet
电子版ISSN：1999-5903
出版年度：2019
卷号：11
期号：2
页码：53-64
DOI：10.3390/fi11020053
出版社：MDPI Publishing
摘要：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.
关键词：ship identification; fully convolutional network; embedded deep learning; scalability ship identification ; fully convolutional network ; embedded deep learning ; scalability