Pipeline for Visual Container Inspection Application using Deep Learning

Guillem Delgado, Andoni Cortés, Estíbaliz Loyo

2022

Abstract

Containerized cargo transportation systems are associated to many visual inspection tasks. Especially during the process of loading and unloading containers from and to the vessel. More and more of these tasks are being automatized in order to speed up the overall process of transportation. This need for optimized processes calls for new vision systems based on the latest technologies to reduce operation times. In this paper, we propose a pipeline and a complete study of each of its parts in order to provide an end-to-end system that solves and automatizes the process of inspection of a loading or unloading freight container from and to the vessel. We outline all the components involved in a separated way. Tackling from the acquisition of the images at the beginning of the process, to visual inspection tasks such as containers’ id detection, text recognition, damage classification or International Maritime Dangerous Goods (IMDG) detection. In addition, we also propose a heuristic algorithm that is capable of managing all the information from the multiple tasks in order to provide as much insights as possible out of the system.

Paper Citation

in Harvard Style

Delgado G., Cortés A. and Loyo E. (2022). Pipeline for Visual Container Inspection Application using Deep Learning. In Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: NCTA; ISBN 978-989-758-611-8, SciTePress, pages 404-411. DOI: 10.5220/0011590900003332

in Bibtex Style

@conference{ncta22,
author={Guillem Delgado and Andoni Cortés and Estíbaliz Loyo},
title={Pipeline for Visual Container Inspection Application using Deep Learning},
booktitle={Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: NCTA},
year={2022},
pages={404-411},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011590900003332},
isbn={978-989-758-611-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Computational Intelligence (IJCCI 2022) - Volume 1: NCTA
TI - Pipeline for Visual Container Inspection Application using Deep Learning
SN - 978-989-758-611-8
AU - Delgado G.
AU - Cortés A.
AU - Loyo E.
PY - 2022
SP - 404
EP - 411
DO - 10.5220/0011590900003332
PB - SciTePress