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Methodology for generating synthetic labeled datasets for visual container inspection

Author

Listed:
  • Delgado, Guillem
  • Cortés, Andoni
  • García, Sara
  • Loyo, Estíbaliz
  • Berasategi, Maialen
  • Aranjuelo, Nerea

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.

Suggested Citation

  • Delgado, Guillem & Cortés, Andoni & García, Sara & Loyo, Estíbaliz & Berasategi, Maialen & Aranjuelo, Nerea, 2023. "Methodology for generating synthetic labeled datasets for visual container inspection," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 175(C).
    • Handle: RePEc:eee:transe:v:175:y:2023:i:c:s136655452300162x
    DOI: 10.1016/j.tre.2023.103174
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    References listed on IDEAS

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    1. Sergey I. Nikolenko, 2021. "Synthetic Data for Deep Learning," Springer Optimization and Its Applications, Springer, number 978-3-030-75178-4, December.
    2. Zixin Wang & Jing Gao & Qingcheng Zeng & Yuhui Sun, 2021. "Multitype Damage Detection of Container Using CNN Based on Transfer Learning," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-12, August.
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