Abstract
Identification of license plates on intermodal containers (or containers) while entering and departing from the yard provides a wide range of practical benefits, such as organizing automatic opening of the rising arm barrier at the entrance and exit to and from the site. In addition, automatic container code recognition can also assist in thwarting the entrance of unauthorized vehicles into the territory. With the recent development of AI, this process is preferably automatic. However, the poor quality of images obtained from surveillance cameras might have detrimental effects on AI models. To deal with this problem, we present a pipeline dubbed as MultiDeep system, which combines several state-of-the-art deep learning models for character recognition and computer vision processes to solve problems of real camera data. We have also compared our results with other pipeline models on real data and accomplished fairly positive results. In this paper, without further references, we will only consider intermodal containers when referring to them as containers.
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We are grateful to CyberLogitec company for funding and providing real dataset for this research.
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Nguyen, D. et al. (2020). Automatic Container Code Recognition Using MultiDeep Pipeline. In: Hernes, M., Wojtkiewicz, K., Szczerbicki, E. (eds) Advances in Computational Collective Intelligence. ICCCI 2020. Communications in Computer and Information Science, vol 1287. Springer, Cham. https://doi.org/10.1007/978-3-030-63119-2_12
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DOI: https://doi.org/10.1007/978-3-030-63119-2_12
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