Computing the Orientation of Hardware Components from Images Using Traditional Computer Vision Methods

This paper introduces a methodology for precise object orientation determination using principal component analysis, with robust performance under significant noise conditions. It validates the potential to mitigate the challenges associated with axis-aligned bounding boxes in smart manufacturing en...

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Bibliographic Details
Published in:	Engineering proceedings Vol. 65; no. 1; p. 8
Main Authors:	Antonio Gambale, Emmett Kerr, Dermot Kerr, Sonya Coleman
Format:	Journal Article
Language:	English
Published:	MDPI AG 01-03-2024
Subjects:	machine learning manufacturing PCA vision
Online Access:	Get full text
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Summary:	This paper introduces a methodology for precise object orientation determination using principal component analysis, with robust performance under significant noise conditions. It validates the potential to mitigate the challenges associated with axis-aligned bounding boxes in smart manufacturing environments. The proposed approach paves the way for improved alignment in robotic grasping tasks, positioning it as a computationally efficient alternative to ML methods employing oriented bounding boxes. the methodology demonstrated a maximum angle deviation of 3.5 degrees under severe noise conditions through testing with bolts in orientations of 0 to 180 degrees.
ISSN:	2673-4591
DOI:	10.3390/engproc2024065008