Issue |
MATEC Web Conf.
Volume 132, 2017
XIII International Scientific-Technical Conference “Dynamic of Technical Systems” (DTS-2017)
|
|
---|---|---|
Article Number | 05014 | |
Number of page(s) | 5 | |
Section | Cognitive methods of heterogeneous data analysis | |
DOI | https://doi.org/10.1051/matecconf/201713205014 | |
Published online | 31 October 2017 |
Defect detection on videos using neural network
DSTU, 344000, Rostov region, Rostov-on-Don, Gagarina 1, Russia
* Corresponding author: roma_sz@mail.ru
In this paper, we consider a method for defects detection in a video sequence, which consists of three main steps; frame compensation, preprocessing by a detector, which is base on the ranking of pixel values, and the classification of all pixels having anomalous values using convolutional neural networks. The effectiveness of the proposed method shown in comparison with the known techniques on several frames of the video sequence with damaged in natural conditions. The analysis of the obtained results indicates the high efficiency of the proposed method. The additional use of machine learning as postprocessing significantly reduce the likelihood of false alarm.
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (http://creativecommons.org/licenses/by/4.0/).
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.