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Using Entropy for Welds Segmentation and Evaluation

Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava, 841 04 Bratislava, Slovakia
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Entropy 2019, 21(12), 1168; https://doi.org/10.3390/e21121168
Received: 24 October 2019 / Revised: 23 November 2019 / Accepted: 26 November 2019 / Published: 28 November 2019
(This article belongs to the Special Issue Entropy in Image Analysis II)
In this paper, a methodology based on weld segmentation using entropy and evaluation by conventional and convolution neural networks to evaluate quality of welds is developed. Compared to conventional neural networks, there is no use of image preprocessing (weld segmentation based on entropy) or data representation for the convolution neural networks in our experiments. The experiments are performed on 6422 weld image samples and the performance results of both types of neural network are compared to the conventional methods. In all experiments, neural networks implemented and trained using the proposed approach delivered excellent results with a success rate of nearly 100%. The best results were achieved using convolution neural networks which provided excellent results and with almost no pre-processing of image data required.
Keywords: weld segmentation; local entropy filter; weld evaluation; convolution neural network; image entropy; Python; Keras; RSNNS; MXNet weld segmentation; local entropy filter; weld evaluation; convolution neural network; image entropy; Python; Keras; RSNNS; MXNet
MDPI and ACS Style

Haffner, O.; Kučera, E.; Drahoš, P.; Cigánek, J. Using Entropy for Welds Segmentation and Evaluation. Entropy 2019, 21, 1168.

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