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A first step towards automated image-based container inspections
Citation Link: https://doi.org/10.15480/882.3122
Publikationstyp
Conference Paper
Publikationsdatum
2020-09-23
Sprache
English
Author
Herausgeber*innen
Institut
TORE-URI
First published in
Number in series
29
Start Page
427
End Page
456
Citation
Hamburg International Conference of Logistics (HICL) 29: 427-456 (2020)
Contribution to Conference
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 recogni-tion 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.
Schlagworte
Logistics
Industry 4.0
Digitalization
Innovation
Supply Chain Management
Artificial Intelligence
Data Science
DDC Class
330: Wirtschaft
Publication version
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Kloever et al. (2020) - A first step towards automated image-based container inspections.pdf
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