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Proceedings Paper

Deep learning in assessment of drill condition on the basis of images of drilled holes
Author(s): Jaroslaw Kurek; Bartosz Swiderski; Albina Jegorowa; Michal Kruk; Stanislaw Osowski
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Paper Abstract

This paper presents novel approach to drill condition assessment using deep learning. The assessment regarding level of the drill wear is done on the basis of the drilled hole images. Two states of the drill are taken into account: the sharp enough to continue production and worn out. The decision is taken on the basis of the shape of hole and also the level of hole shredding. In this way the drill condition is associated with the problem of image analysis and classification. Novel approach to this classification task in the form of deep learning has been applied in solving this problem. The important advantage of this method is great simplification of the recognition procedure, since any handy craft prepared features are not needed and the focus may be concentrated on the most interesting aspects of data mining and machine learning. The obtained results belong to the best in comparison to other approaches to the problem solution.

Paper Details

Date Published: 8 February 2017
PDF: 7 pages
Proc. SPIE 10225, Eighth International Conference on Graphic and Image Processing (ICGIP 2016), 102251V (8 February 2017); doi: 10.1117/12.2266254
Show Author Affiliations
Jaroslaw Kurek, Warsaw Univ. of Life Sciences (Poland)
Bartosz Swiderski, Warsaw Univ. of Life Sciences (Poland)
Albina Jegorowa, Warsaw Univ. of Life Sciences (Poland)
Michal Kruk, Warsaw Univ. of Life Sciences (Poland)
Stanislaw Osowski, Warsaw Univ. of Technology (Poland)
Military Univ. of Technology (Poland)

Published in SPIE Proceedings Vol. 10225:
Eighth International Conference on Graphic and Image Processing (ICGIP 2016)
Yulin Wang; Tuan D. Pham; Vit Vozenilek; David Zhang; Yi Xie, Editor(s)

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