Methodology for generating synthetic labeled datasets for visual container inspection

TL;DR

This paper introduces a novel methodology to generate realistic synthetic labeled datasets for visual container inspection, enabling effective training of deep neural networks in real-world dock environments.

Contribution

The paper presents an innovative approach to create synthetic, balanced, and labeled datasets for container inspection, including the first open synthetic dataset SeaFront.

Findings

Synthetic dataset enables effective neural network training.

Validation across multiple visual tasks shows high accuracy.

Provides the first open synthetic dataset SeaFront.

Abstract

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.