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A first step towards automated image-based container inspections

In: Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Logistics (HICL), Vol. 29

Author

Listed:
  • Klöver, Steffen
  • Kretschmann, Lutz
  • Jahn, Carlos

Abstract

Purpose: The visual inspection of freight containers at depots is an essential part of the maintenance and repair process, which ensures that containers are in a suitable condition for loading and safe transport. Currently this process is done manually, which has certain disadvantages and insufficient availability of skilled inspectors can cause delays and poor predictability. Methodology: This paper addresses the question whether instead computer vision algorithms can be used to automate damage recognition based on digital images. The main idea is to apply state-of-the-art deep learning methods for object recognition on a large dataset of annotated images captured during the inspection process in order to train a computer vision model and evaluate its performance. Findings: The focus is on a first use case where an algorithm is trained to predict the view of a container shown on a given picture. Results show robust performance for this task. Originality: The originality of this work arises from the fact that computer vision for damage recognition has not been attempted on a similar dataset of images captured in the context of freight container inspections.

Suggested Citation

  • Klöver, Steffen & Kretschmann, Lutz & Jahn, Carlos, 2020. "A first step towards automated image-based container inspections," Chapters from the Proceedings of the Hamburg International Conference of Logistics (HICL), in: Kersten, Wolfgang & Blecker, Thorsten & Ringle, Christian M. (ed.), Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain. Proceedings of the Hamburg International Conference of Lo, volume 29, pages 427-456, Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management.
    • Handle: RePEc:zbw:hiclch:228929
    DOI: 10.15480/882.3122
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    Cited by:

    1. Christoph Martius & Lutz Kretschmann & Miriam Zacharias & Carlos Jahn & Ole John, 2022. "Forecasting worldwide empty container availability with machine learning techniques," Journal of Shipping and Trade, Springer, vol. 7(1), pages 1-24, December.

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