Classification of Surface Fracture in Plastics Using Convolutional Neural Networks

Abstract

Abstract In the present study, we investigate the use of convolutional neural network (CNN) models for classifying the characteristics of surface fractures in plastics, which are affected by environmental stress cracking agents. Nineteen CNN models with different architectures are adopted with 4,012 crack images, and they are evaluated based on the classification accuracy. Four models with a relatively higher accuracy are selected and compared with each performance metric obtained from a confusion matrix. The model with the Inception-ResNet-v2 architecture showed the highest performance metrics value of over 0.96. Although the model with the ResNet-18 architecture showed slightly lower levels of performance metrics, its training time was more than 10 times faster.