mlsanityData to 14 Oct 2026

Methodology for generating synthetic labeled datasets for visual container inspection

arXiv 2306.14584 ↗First posted 26 Jun 2023cs.CVcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 27 of its 50 references that are in the map. First posted on arXiv 26 Jun 2023.

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References · 27

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15 more
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