|Publisher URL:||https://www.epubli.de/shop/buch/Data-Science-and-Innovation-in-Supply-Chain-Management-Wolfgang-Kersten-9783753123462/106047||Title:||A first step towards automated image-based container inspections||Language:||English||Authors:||Klöver, Steffen
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||URI:||http://hdl.handle.net/11420/8012||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)|
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