Design of a Deep Learning Model to Automate Container Entry and Exit at the Cartagena Container Terminal (CONTECAR)
DOI:
https://doi.org/10.18041/2619-4244/dl.36.12539Palabras clave:
Port terminal, Deep Learning, Artificial Intelligence, OCR, RFID, Logistics automation, Container trackingResumen
In ports, process automation such as container identification using technologies such as Optical Character Recognition (OCR) and Radio Frequency Identification (RFID) is essential to speed up operations and reduce human error. The implementation of these systems not only increases competitiveness and security, but also minimizes operational risks and improves agility in the management of logistics flows, which is crucial to adapt to growing demand and ensure the sustainability of port infrastructures.
The present mixed research aims to design a deep learning model to optimize the container entry and exit processes at the Cartagena Container Terminal (CONTECAR). The model will be designed considering the existing infrastructure, technologies implemented to date, available machinery and logistical and organizational characteristics of terminal operating processes.
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