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A HIERARCHICAL MACHINE LEARNING WORKFLOW FOR OBJECT DETECTION OF ENGINEERING COMPONENTS

Published online by Cambridge University Press:  19 June 2023

Lee Kent ,
Chris Snider ,
James Gopsill ,
Mark Goudswaard ,
Aman Kukreja  and
Ben Hick
Lee Kent
Affiliation:
University of Bristol
Chris Snider*
Affiliation:
University of Bristol
James Gopsill
Affiliation:
University of Bristol
Mark Goudswaard
Affiliation:
University of Bristol
Aman Kukreja
Affiliation:
University of Bristol
Ben Hick
Affiliation:
University of Bristol
*
Snider, Chris, University of Bristol, United Kingdom, [email protected]

Abstract

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Machine Learning (ML) techniques are showing increasing use and value in the engineering sector. Object Detection methods, by which an ML system identifies objects from an image presented to it, have demonstrated promise for search and retrieval and synchronised physical/digital version control, amongst many applications.

However, accuracy of detection often decreases as the number of objects considered by the system increases which, combined with very high training times and computational overhead, makes widespread use infeasible.

This work presents a hierarchical ML workflow that leverages the pre-existing taxonometric structures of engineering components and abundant digital models (CAD) to streamline training and increase accuracy. With a two-layer structure, the approach demonstrates potential to increase accuracy to >90%, with potential time savings of 75% and greatly increased flexibility and expandability.

While further refinement is required to increase robustness of detection and investigate scalability, the approach shows significant promise to increase feasibility of Object Detection techniques in engineering.

Keywords

Machine learningArtificial intelligenceObject DetectionProduct Lifecycle Management (PLM)
Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 201 - 210
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2023. Published by Cambridge University Press

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