  Nowadays, containerized freight transport is one of the most important
transportation systems that is undergoing an automation process due to the Deep
Learning success. However, it suffers from a lack of annotated data in order to
incorporate state-of-the-art neural network models to its systems. In this
paper we present an innovative methodology to generate a realistic, varied,
balanced, and labelled dataset for visual inspection task of containers in a
dock environment. In addition, we validate this methodology with multiple
visual tasks recurrently found in the state of the art. We prove that the
generated synthetic labelled dataset allows to train a deep neural network that
can be used in a real world scenario. On the other side, using this methodology
we provide the first open synthetic labelled dataset called SeaFront available
in: https://datasets.vicomtech.org/di21-seafront/readme.txt.

Computer Vision and Pattern Recognition