Methodology for generating synthetic labeled datasets for visual container inspection
This addresses a data scarcity problem for researchers and practitioners in automated container inspection, though it is incremental as it applies existing synthetic data generation techniques to a specific domain.
The paper tackles the lack of annotated data for visual container inspection in automated freight transport by presenting a methodology to generate realistic synthetic labeled datasets, and it proves that training deep neural networks with this data works in real-world scenarios, providing the first open synthetic dataset called SeaFront.
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.