Open AccessCrackWeb : A modified U-Net based segmentation architecture for crack detection
Author(s) -
Sandeep Ghosh,
Subham Singh,
Amit Maity,
Hirak Kumar Maity
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1080/1/012002
Subject(s) - computer science , artificial intelligence , convolutional neural network , residual , architecture , dice , segmentation , convergence (economics) , pattern recognition (psychology) , feature extraction , metric (unit) , deep learning , algorithm , engineering , mathematics , art , operations management , geometry , economics , visual arts , economic growth
Classical image processing methods demands heavy feature engineering, as well as they are not that precise, when it comes to manual extraction of relevant features in real life scenarios amid to various lighting conditions and other factors.Thus, detection of cracks using methods based on classical image processing techniques fails to provide satisfactory results always. Hence, we have proposed a deep convolutional neural network, that is not based on manual extraction of features as mentioned above. We proposed a modified U-Net architecture, and replaced all of its convolutional layers with residual blocks, inspired from the ResNet architecture. For evaluation of our model Dice Loss is used as our objective function and F1 score as a metric. Other than that, for better convergence and optimization, a learning rate scheduler and AMSGRAD optimizer was utilized.