A first step towards automated image-based container inspections

Steffen Klöver, Lutz Kretschmann and Carlos Jahn

A chapter in Data Science and Innovation in Supply Chain Management: How Data Transforms the Value Chain, 2020, pp 427-456 from Hamburg University of Technology (TUHH), Institute of Business Logistics and General Management

Abstract: 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.

Keywords: Logistics; Industry 4.0; Digitalization; Innovation; Supply Chain Management; Artificial Intelligence; Data Science (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:hiclch:228929

DOI: 10.15480/882.3122

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