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Embedded Deep Learning for Ship Detection and Recognition

Author

Listed:
  • 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)

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.

Suggested Citation

  • Hongwei Zhao & Weishan Zhang & Haoyun Sun & Bing Xue, 2019. "Embedded Deep Learning for Ship Detection and Recognition," Future Internet, MDPI, vol. 11(2), pages 1-12, February.
    • Handle: RePEc:gam:jftint:v:11:y:2019:i:2:p:53-:d:207920
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    Citations

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    Cited by:

    1. Salvatore Graziani & Maria Gabriella Xibilia, 2020. "Innovative Topologies and Algorithms for Neural Networks," Future Internet, MDPI, vol. 12(7), pages 1-4, July.
    2. Vidhi Tiwari & Kirti Pal, 2022. "Short-Term Load Forecasting for a Captive Power Plant Using Artificial Neural Network," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 12(1), pages 1-11, January.
    3. Fabíola Martins Campos de Oliveira & Edson Borin, 2019. "Partitioning Convolutional Neural Networks to Maximize the Inference Rate on Constrained IoT Devices," Future Internet, MDPI, vol. 11(10), pages 1-30, September.

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