Production-Ready End-to-End Visual Quality Inspection for Defect Detection on Surfaces Based on a Multi-Stage AI System

Patrick Trampert, Tobias Masiak, Felix Schmidt, Nicolas Thewes, Tim Kruse, Christian Witte, Georg Schneider

2024

Abstract

Quality inspection based on optical systems is often limited by the ability of conventional image processing pipelines. Moreover, setting up such a system in production must be tailored towards specific tasks, which is a very tedious, time-consuming, and expensive work that is rarely transferable to different inspection problems. We present a configurable multi-stage system for Visual Quality Inspection (VQI) based on Artificial Intelligence (AI). In addition, we develop a divide-and-conquer strategy to break down complex tasks into sub-problems that are easy-to-handle with well-understood AI approaches. For data acquisition a human-machine-interface is implemented via a graphical user interface running at production side. Besides facilitated AI processing the evolved strategy leads to a knowledge digitalisation through sub-problem annotation that can be transferred to future use cases for defect detection on surfaces. We demonstrate the AI based quality inspection potential in a production use case, where we were able to reduce the false-error-rate from 16.83% to 2.80%, so that our AI workflow has already replaced the old system in a running production.

Paper Citation

in Harvard Style

Trampert P., Masiak T., Schmidt F., Thewes N., Kruse T., Witte C. and Schneider G. (2024). Production-Ready End-to-End Visual Quality Inspection for Defect Detection on Surfaces Based on a Multi-Stage AI System. In Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE; ISBN 978-989-758-693-4, SciTePress, pages 55-66. DOI: 10.5220/0012536500003720

in Bibtex Style

@conference{improve24,
author={Patrick Trampert and Tobias Masiak and Felix Schmidt and Nicolas Thewes and Tim Kruse and Christian Witte and Georg Schneider},
title={Production-Ready End-to-End Visual Quality Inspection for Defect Detection on Surfaces Based on a Multi-Stage AI System},
booktitle={Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE},
year={2024},
pages={55-66},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012536500003720},
isbn={978-989-758-693-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 4th International Conference on Image Processing and Vision Engineering - Volume 1: IMPROVE
TI - Production-Ready End-to-End Visual Quality Inspection for Defect Detection on Surfaces Based on a Multi-Stage AI System
SN - 978-989-758-693-4
AU - Trampert P.
AU - Masiak T.
AU - Schmidt F.
AU - Thewes N.
AU - Kruse T.
AU - Witte C.
AU - Schneider G.
PY - 2024
SP - 55
EP - 66
DO - 10.5220/0012536500003720
PB - SciTePress