期刊
MATERIALI IN TEHNOLOGIJE
卷 51, 期 1, 页码 123-131出版社
INST ZA KOVINSKE MATERIALE I IN TEHNOLOGIE
DOI: 10.17222/mit.2015.335
关键词
convolutional neural networks; classification; surface defects; steel sheet; convolutional kernels; sparse auto-encoder
资金
- Natural Science Foundation of China [51174151]
- Specialized Research Fund for the Key Science and Technology Innovation Plan of Hubei Province [2013AAA011]
A convolutional neural network (CNN) is proposed to learn multiple useful feature representations for a classification from low level (raw pixels) to high level (object). Convolutional kernels are initialized by the learned filter kernels that come from sparse auto-encoders. Unlike some traditional methods, which divide the feature abstracting and classifier training into two separated processes, a discriminative feature vector and a single multi-class classifier of softmax regression are learned simultaneously during the training process. Based on the learned high-quality feature representation, the classification can be efficiently performed. A real-world case of surface defects on steel sheet, which evaluates the classification performance of the proposed method, is depicted in detail. The experimental results indicate that the proposed method is quite simple, effective and robustness for the classification of surface defects on hot-rolled steel sheet.
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