Abstract:
Depth Estimations a crucial technique to get rid of sudden failure of a Clay-Made
Home. It can happen for many reasons such as poor construction, temperature
changes suddenly, extra loaded etc. Automatic detection of cracks is an important
task to maintenance for safety life. It’s a challenging task due to intensity of cracks
and complexity of the background. A supervised deep convolutional neural network
is trained to detect the cracks. After that Naive Bayes is used to calculate the depth
based on height and width .For research here I have taken 600 images of size
720*480, collected by smart phones. Manual crack detection is takes more time and
accuracy is low. It is one of the simplest way to detect a crack and estimate the depth.
It is very important to detect and estimate the Depth. If we cannot detect the crack,
crack will make a serious damage. The objective of this research is to develop a
simple model based on crack image. Firstly, Ihave taken cracked images, after that
prepressed the images. Prepressed images convert into Gray images and improve
contrast.
