**Hongwei Zhao, Weishan Zhang \*, Haoyun Sun and Bing Xue**

College of Computer and Communication Engineering, China University of Petroleum (UPC), Qingdao 266580, China; upcvagen@163.com (H.Z.); hys\_upc@163.com (H.S.); xueb2016@163.com (B.X.)

**\*** Correspondence: zhangws@upc.edu.cn

Received: 31 December 2018; Accepted: 31 January 2019; Published: 21 February 2019

**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
