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Title: A first step towards automated image-based container inspections
Language: English
Authors: Klöver, Steffen 
Kretschmann, Lutz 
Jahn, Carlos 
Editor: Kersten, Wolfgang  
Blecker, Thorsten 
Ringle, Christian M.  
Keywords: Logistics;Industry 4.0;Digitalization;Innovation;Supply Chain Management;Artificial Intelligence;Data Science
Issue Date: 23-Sep-2020
Source: Hamburg International Conference of Logistics (HICL) 29: 427-456 (2020)
Part of Series: Proceedings of the Hamburg International Conference of Logistics (HICL) 
Volume number: 29
Abstract (english): 
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.
Conference: Hamburg International Conference of Logistics (HICL) 2020 
DOI: 10.15480/882.3122
ISBN: 978-3-753123-46-2
ISSN: 2365-5070
Institute: Maritime Logistik W-12 
Document Type: Chapter/Article (Proceedings)
License: CC BY-SA 4.0 (Attribution-ShareAlike 4.0) CC BY-SA 4.0 (Attribution-ShareAlike 4.0)
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