Abstract: Convolutional neural network is widely used in image processing.In order to effectively inspect glass surface defects in production activities,the principle of machine learning based on convolutional neural network was analyzed.An image recognition model based on Multiscale Convolution Neural Network (MCNN) was proposed.Then,the application of MCNN model in the identification of glass surface defects was studied,and comparison experiments were carried out by using different algorithms and classifiers.Furthermore,confusion matrix and F1 values to evaluate learner performance were used to evaluate the performance of learner.Experiment results showed that the designed MCNN was more accurate than the traditional Convolutional Neural Networks (CNN) recognition method,especially in the recognition accuracy of scratch defects and impurity defect images,F1 values were increased by more than 5.0%.Obviously,by comparing with the traditional CNN,MCNN is superior in the overall recognition accuracy of glass defect detection.

Key words: convolution neural network, machine learning, Softmax regression, support vector machine, glass defect detection

摘要： 卷积神经网络在图像处理中的应用越来越广泛,针对图像处理技术手段在玻璃生产表面缺陷有效检验,分析了基于卷积神经网络的机器学习原理与方法,提出一种基于多尺度卷积神经网络(MCNN)图像识别模型,将MCNN模型在玻璃表面缺陷识别中进行应用实践研究,通过采用不同的算法模型和分类器进行对比实验,并运用混淆矩阵和F1值来评估学习器性能。实验结果表明,所设计的MCNN均比传统卷积神经网络(CNN)识别方法的准确率较高,尤其是在划痕缺陷和杂质缺陷图像的识别准确率上提高了较大的幅度,F1值均提高了5.0%以上,在玻璃缺陷检测的整体识别准确率上较优。

关键词: 卷积神经网络, 机器学习, Softmax回归, 支持向量机, 玻璃缺陷检测