Revolutionizing seaport operations is imperative for rapid urbanization in housing, transportation, education, health, and the economy. For this, the integration of artificial intelligence (AI) and computer vision (CV) technology is required. With smart traffic monitoring and parking space distribution, smart mobility optimization is realized for intelligent cities. Seaport container transportation is also required using AI and CV technologies. Thus, we established a sustainable and efficient parking system at seaports with a comprehensive framework based on deep learning and transfer learning techniques. Through an in-depth literature review, we determined the benefits of combining AI and CV to optimize parking facility management. Then, data gathering, quality evaluation, and practical applications of CV and AI in parking spaces and lanes were conducted. The significance of AI and CV technologies in developing efficient and sustainable transportation systems was verified by enhancing container drayage efficiency in seaports. The proposed system allowed AI and CV technologies to improve traffic management, optimize parking space allocation, and streamline container movement within seaports. By using deep learning techniques, particularly transfer learning, the proposed framework accurately identified parking spaces and lanes considering the crane position. CCTV camera data were used for real-time monitoring and efficient utilization of parking spaces in the seaport. Implementation of this framework enhanced container drayage operations, improved operational efficiency, reduced congestion, and enhanced seaport management. The proposed transformative technologies are crucial for the development of sustainable and efficient transportation networks for intelligent port operations.


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    Titel :

    Enhancing Parking Facility of Container Drayage in Seaports: A Study on Integrating Computer Vision and AI


    Beteiligte:
    Lee, Hoon (Autor:in) / Chatterjee, Indranath (Autor:in) / Cho, Gyusung (Autor:in)


    Erscheinungsdatum :

    11.08.2023


    Format / Umfang :

    1338897 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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