Abstract-We present a Max-Pooling Convolutional Neural Network approach for supervised steel defect classification. On a classification task with 7 defects, collected from a real production line, an error rate of 7% is obtained. Compared to SVM classifiers trained on commonly used feature descriptors our best net performs at least two times better. Not only we do obtain much better results, but the proposed method also works directly on raw pixel intensities of detected and segmented steel defects, avoiding further time consuming and hard to optimize ad-hoc preprocessing.
Moreover, in REF , a max-pooling convolutional neural network is applied for steel defect classification.
Steel defect classification with Max-Pooling Convolutional Neural Networks
The 2012 International Joint Conference on Neural Networks (IJCNN)
The 2012 International Joint Conference on Neural Networks (IJCNN)
Computer Science