Back to articles
Articles
Volume: 33 | Article ID: art00008
Image
Investigation of Different Illumination Scenarios for the Evaluation of Thermally Cut Edges with Convolutional Neural Networks using a Mobile Device
  DOI :  10.2352/ISSN.2470-1173.2021.6.IRIACV-314  Published OnlineJanuary 2021
Abstract

In sheet metal production, the quality of a cut edge determines the quality of the cut itself. Quality criteria such as the roughness, the edge slope, and the burr height are of decisive importance for further application and quality determination. In order to be able to determine these criteria analytically, the depth information of the edge must be determined at great expense. The current methods for obtaining the depth information are very time-consuming, require laboratory environments and are therefore not suitable for a fast evaluation of the quality criteria. Preliminary work has shown that it is possible to make robust and accurate statements about the roughness of a cut edge based on images when using an industrial camera with a standard lens and diffuse incident light, if the model used for this purpose has been trained on appropriate images. In this work, the focus is on the illumination scenarios and their influence on the prediction quality of the models. Images of cut edges are taken under different defined illumination scenarios and it is investigated whether a comprehensive evaluation of the cut edges on the evaluation criteria defined in standards is possible under the given illumination conditions. The results of the obtained model predictions are compared with each other in order to make a statement about the importance of the illumination scenario. In order to investigate the possibility of a mobile low-cost evaluation of cut edges, cheap hardware components for illumination and a smartphone for image acquisition are used.

Subject Areas :
Views 50
Downloads 11
 articleview.views 50
 articleview.downloads 11
  Cite this article 

Janek Stahl, Christian Jauch, Macit Tuncel, Marco Huber, "Investigation of Different Illumination Scenarios for the Evaluation of Thermally Cut Edges with Convolutional Neural Networks using a Mobile Devicein Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision,  2021,  pp 314-1 - 314-7,  https://doi.org/10.2352/ISSN.2470-1173.2021.6.IRIACV-314

 Copy citation
  Copyright statement 
Copyright © Society for Imaging Science and Technology 2021
72010604
Electronic Imaging
2470-1173
Society for Imaging Science and Technology
IS&T 7003 Kilworth Lane Springfield, VA 22151 USA