Early Defect Detection in Conveyor Belts using Machine Vision

Guilherme Netto, Guilherme Netto, Bruno Coelho, Bruno Coelho, Saul Delabrida, Amilton Sinatora, Héctor Azpúrua, Gustavo Pessin, Ricardo Oliveira, Andrea Bianchi

Abstract

Continuous belt monitoring is of utmost importance since wears on its surface can develop into tears and even rupture. It can causes the interruption of the conveyor, and consequently, loss of capital, or even worse, serious or fatal accidents. This paper proposes a laser-based machine vision method for detecting defects in conveyor belts to solve the monitoring problem. The approach transforms an image of a laser line into a one-dimensional signal, then analyzes it to detect defects, considering that variations in this signal are caused by defects/imperfections on the belt surface. Differently from previous works, the proposed method can identify a defect through a 2D reconstruction of it. The results reveal that the proposed method was capable to detect superficial imperfections in simulated conveyor belt experiments, achieving high values in metrics such as precision and recall.

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Paper Citation


in Harvard Style

Netto G., Coelho B., Delabrida S., Sinatora A., Azpúrua H., Pessin G., Oliveira R. and Bianchi A. (2021). Early Defect Detection in Conveyor Belts using Machine Vision.In Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP, ISBN 978-989-758-488-6, pages 303-310. DOI: 10.5220/0010240803030310


in Bibtex Style

@conference{visapp21,
author={Guilherme Netto and Bruno Coelho and Saul Delabrida and Amilton Sinatora and Héctor Azpúrua and Gustavo Pessin and Ricardo Oliveira and Andrea Bianchi},
title={Early Defect Detection in Conveyor Belts using Machine Vision},
booktitle={Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,},
year={2021},
pages={303-310},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010240803030310},
isbn={978-989-758-488-6},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 4: VISAPP,
TI - Early Defect Detection in Conveyor Belts using Machine Vision
SN - 978-989-758-488-6
AU - Netto G.
AU - Coelho B.
AU - Delabrida S.
AU - Sinatora A.
AU - Azpúrua H.
AU - Pessin G.
AU - Oliveira R.
AU - Bianchi A.
PY - 2021
SP - 303
EP - 310
DO - 10.5220/0010240803030310