Container-code recognition system based on computer vision and deep neural networks
Yi Liu,Tianjian Li,Li Jiang,Xiaoyao Liang +3 more
- Vol. 1955, Iss: 1, pp 040118
TLDR
An automatic container-code recognition system based on computer vision and deep neural networks is proposed, which is able to deal with more situations, and generates a better detection result through combination to avoid the drawbacks of the two methods.Abstract:
Automatic container-code recognition system becomes a crucial requirement for ship transportation industry in recent years. In this paper, an automatic container-code recognition system based on computer vision and deep neural networks is proposed. The system consists of two modules, detection module and recognition module. The detection module applies both algorithms based on computer vision and neural networks, and generates a better detection result through combination to avoid the drawbacks of the two methods. The combined detection results are also collected for online training of the neural networks. The recognition module exploits both character segmentation and end-to-end recognition, and outputs the recognition result which passes the verification. When the recognition module generates false recognition, the result will be corrected and collected for online training of the end-to-end recognition sub-module. By combining several algorithms, the system is able to deal with more situations, and the online training mechanism can improve the performance of the neural networks at runtime. The proposed system is able to achieve 93% of overall recognition accuracy.read more
Citations
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Journal ArticleDOI
An Adaptive Deep Learning Framework for Shipping Container Code Localization and Recognition
TL;DR: An adaptive deep learning framework for shipping container code localization and recognition and it is demonstrated that the proposed framework achieved better localization performance and obtained 93.33% recognition accuracy.
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Development of computer vision informed container crane operator alarm methods
TL;DR: Wang et al. as discussed by the authors developed a container color detection model to predict the color of the container being unloaded and then used the prediction results to develop two crane operator alarm methods, one alerts the crane operator if the detected color of a container is not in compliance with the correct container color.
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Improving Data Quality in the Cargo Industry with Modern Recurrent Neural Network Architecture
TL;DR: An approach to solving key issues in two important facets of the supply chain, predicting the date of restitution for a cargo container and using an optical character recognition (OCR)-centred pipeline to extrapolate data from containers are presented.
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Service quality improvement strategy for container terminal using SERVQUAL and house of quality
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Design and Implementation for BIC Code Recognition System of Containers using OCR and CRAFT in Smart Logistics
TL;DR: In this article , the authors proposed the design and implementation of a BIC Code recognition system using an open source-based OCR engine, deep learning object detection algorithm, and text detector model.
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