基于改进AlexNet模型的抓毛织物质量检测方法

针对传统图像识别方法对抓毛织物表面特征难以提取且识别准确率低的问题,提出了一种改进AlexNet模型的抓毛织物质量检测方法,通过数据增强方法对抓毛织物数据进行扩充,构建卷积神经网络对抓毛织物的样本特征进行提取,利用SGDM、RMSProp、Adam优化算法和改变学习率相结合的实验方法,采用全新学习与迁移学习两种算法对抓毛织物图像数据集进行训练,在训练完成后,分别利用卷积神经网络的不同深度池化层提取抓毛织物样本的特征作为输入,将提取到的抓毛织物特征拟合支持向量机(SVM)分类器,最后对输入的抓毛织物图像进行分类。实验结果表明:使用卷积神经网络方法能够增加卷积层对抓毛织物表面特征的提取能力,获得具有较高分辨力的图像特征,通过数据增强和SGDM算法训练的模型,提取网络pool5层特征拟合SVM分类器,识别准确率明显提高。基于改进AlexNet模型的抓毛织物质量检测方法能够提取抓毛织物表面特征且识别率高。

An improved AlexNet model for fleece fabric quality inspection

The traditional image recognition method is difficult to extract the surface features of the fleece fabrics leading to low recognition accuracy. This study proposed an improved AlexNet model for the quality detection method of the fleece fabrics. The convolutional neural network was used to extract the sample features of the fleece fabric, and the experimental method combining SGDM, RMSProp, Adam optimization algorithm was adopted for the study to investigate effects of changing learning rate and the use of two new learning and transfer learning algorithms in training the fleece fabric image dataset. After the completion of training, different depth pooling layers of the convolutional neural network were employed to extract the features of the fleece fabric samples. The extracted fleece fabric features were fitted to the support vector machine(SVM) classifier to analyze the input fleece fabric image. The experimental results show that the use of the convolutional neural network method can increase the ability of the convolutional layer to extract the surface features of the fleece fabric, and obtain image features with higher resolution. The model trained by the data enhancement and SGDM algorithm can extract the network pool5 layer features. With the SVM classifier, the recognition accuracy enhanced significantly. The quality detection method of fleece fabrics based on the improved AlexNet model can extract the surface features of fleece fabrics with high recognition rate.