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.

翻译：如今，集装箱化货物运输是最重要的运输系统之一，因深度学习技术的成功而正经历自动化进程。然而，在将最先进的神经网络模型集成到其系统时，该系统面临标注数据匮乏的困境。本文提出一种创新方法，用于生成面向码头环境中集装箱视觉检测任务的真实、多样、均衡且带标注的数据集。此外，我们通过多种现有文献中常见的视觉任务对该方法进行验证。实验证明，基于所生成的合成标注数据集训练的深度神经网络可在真实场景中应用。同时，利用本方法，我们首次公开了名为SeaFront的合成标注数据集，访问地址为：https://datasets.vicomtech.org/di21-seafront/readme.txt。