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.
Highlights
With the development of the marine economy, marine transportation and management have been attracting more and more attention in modern ports [1]
Surveillance cameras are increasingly deployed for port management and security in order to realize a smart port [2]. This is challenging due to complex ship profiles, ship background and object occlusions, variations of weather and light conditions, and other issues
The work in [4] handled Chinese car license plate recognition from traffic videos with image features extracted by DCNNs (Deep Convolutional Neural Networks)
Summary
With the development of the marine economy, marine transportation and management have been attracting more and more attention in modern ports [1]. Surveillance cameras are increasingly deployed for port management and security in order to realize a smart port [2]. This is challenging due to complex ship profiles, ship background and object occlusions, variations of weather and light conditions, and other issues. License plate recognition [5] based on deep learning was used for feature extraction and classification. This regular character recognition is much simpler than these Chinese characters from ship license plates, due to the usage of various character types and complex backgrounds, and the variations of ship plate locations
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