Klöver, SteffenSteffenKlöverKretschmann, LutzLutzKretschmannJahn, CarlosCarlosJahn2020-11-302020-11-302020-09-23Hamburg International Conference of Logistics (HICL) 29: 427-456 (2020)http://hdl.handle.net/11420/8012Purpose: 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.enhttps://creativecommons.org/licenses/by-sa/4.0/LogisticsIndustry 4.0DigitalizationInnovationSupply Chain ManagementArtificial IntelligenceData ScienceWirtschaftConference Paper10.15480/882.3122https://www.epubli.de/shop/buch/Data-Science-and-Innovation-in-Supply-Chain-Management-Wolfgang-Kersten-9783753123462/10604710.15480/882.3122Kersten, WolfgangWolfgangKerstenBlecker, ThorstenThorstenBleckerRingle, Christian M.Christian M.RingleConference Paper