Pipeline for Visual Container Inspection Application using Deep Learning

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.