A first step towards automated image-based container inspections

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.

Subjects

Logistics

Industry 4.0

Digitalization

Innovation

Supply Chain Management

Artificial Intelligence

Data Science

DDC Class

330: Wirtschaft

Publication version

publishedVersion

Lizenz

https://creativecommons.org/licenses/by-sa/4.0/

Name

Kloever et al. (2020) - A first step towards automated image-based container inspections.pdf

Size

1.26 MB

Format

Adobe PDF

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A first step towards automated image-based container inspections