Open AccessPerformance Analysis of Weld Image Classification Using Modified Resnet CNN Architecture
Author(s) -
L. Mohanasundari
Publication year - 2021
Publication title -
türk bilgisayar ve matematik eğitimi dergisi
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.218
H-Index - 3
ISSN - 1309-4653
DOI - 10.17762/turcomat.v12i2.1943
Subject(s) - convolutional neural network , pooling , computer science , artificial intelligence , welding , pattern recognition (psychology) , architecture , layer (electronics) , deep learning , contextual image classification , residual neural network , image (mathematics) , computer vision , engineering , materials science , mechanical engineering , art , composite material , visual arts
The detection and classifications of weld images is important for improving the quality of the joined materials during production period. In order to automate the classification of weld images in industry, this paper proposes an effective automatic method for the detection and classifications of the weld images into four different cases using deep learning methods. In this work, Convolutional Neural Network (CNN) is adopted for the weld image classification by modifying the internal architecture of the CNN architecture. This proposed ResNet CNN architecture is designed with three Convolutional layers, two numbers of pooling layers with activation layer and two numbers of Fully Connected Neural Networks (FCNN).The FCNN in proposed CNN architecture is designed with 15 internal hidden layers and each hidden layer is designed with 20 neurons which obtains high classification efficiency. The morphological activity functional methods are used on the classified weld images to detect the crack regions.