Automatic Brittle Fracture Ratio Estimation Using Convolutional Neural Network Regression Based on Classmap Regulation

Abstract

A convolutional neural network (CNN) based regression is proposed for estimating the brittle fracture ratio (BFR) in a fracture image of a drop weight tear test (DWTT) specimen. Different with the previous complex semantic segmentation-based estimator, the method extracts the feature vector through global average pooling of feature map and calculates the BFR directly through the fully connected layer. By removing decoder network, the number of weights, training time, and required GPU memory dramatically reduced. To train the proposed CNN, a new loss function, which is the sum of L1-norm between class activation map and ground truth inspection image and L1-norm of BFR error, is also designed. To validate the present method, fracture images of 1532, 79, and 158 DWTT specimens obtained from real industrial site were used for training, validation, and test, respectively. The accuracy of the proposed method was evaluated based on the number of test samples with an error of 5% or less divided by the total number of test samples, which is the measure used in real industrial application. Despite having dramatically reduced the number of weights and inference time by 85.8% and 64.8%, respectively, the proposed method has a higher accuracy (96.2%) compared to that of the existing segmentation based BFR estimation method (94.9%).