Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: http://hdl.handle.net/10419/228929
Autoren: 
Klöver, Steffen
Kretschmann, Lutz
Jahn, Carlos
Datum: 
2020
Quellenangabe: 
[Editor:] Kersten, Wolfgang [Editor:] Blecker, Thorsten [Editor:] Ringle, Christian M. [Title:] 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 [ISBN:] 978-3-7531-2346-2 [Year:] 2020 [Pages:] 427-456
Zusammenfassung: 
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.
Schlagwörter: 
Logistics
Industry 4.0
Digitalization
Innovation
Supply Chain Management
Artificial Intelligence
Data Science
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
https://creativecommons.org/licenses/by-sa/4.0/
Dokumentart: 
Conference Paper

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