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: this https URL

本文介绍了一种创新的方法来生成一个逼真、多样、平衡和带标签的数据集，用于集装箱在码头环境下的视觉检查任务，并且我们证明了生成的合成标记数据集可以用于训练可以在实际环境中使用的深度神经网络。此外，我们提供了第一个开放的合成标记数据集 “SeaFront”。