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Paper Details

Paper Title
BRIDGE CRACK DETECTION USING CONVOLUTIONAL NEURAL NETWORK
Abstract
To day of the week, identifying cracks in bridges and responsible bridge conditions primarily involve manual labour. Bridge inspection by anthropological experts has some drawbacks such in place of the inability to physically examine all parts of the bridge, sole dependency on the expert knowledge of the bridge superintendent. Additionally it requires proper training of the human resource and complete the aforementioned is not cost effective. This article proposes an automatic bridge examination attitude manipulating wavelet-based image features lengthwise by means of CNN for automatic detection of cracks in conduit images. A two-stage method is followed, someplace in the principal stage a decision is finished as whether a doppelgänger should go through a pre-processing step (depending on image characteristics), and glowing along in the second stage, wavelet features are pull out on or after the image using a upward window-based procedure.
Keyword
Crack detection, Image processing, Segmentation, Feature extraction
Others Details
Paper Id : 55393
Author Name : MANALI ADAKE
Co-Author Name(s) : manali adakemanu pawlemayuri sadaphule
Volume/Issue No : Volume 07 Issue 07
Page No : 18-21
DOI Number : DOI:10.21090/IJAERD.55393
License : This work is licensed under a Creative Commons Attribution 4.0 International License.
website : http://www.ijaerd.com/index.php
Impact Factor : 5.71, SJIF-2017
ISSN Details : eISSN: 2348-4470, pISSN:2348-6406
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