基于改进深度神经网络的纱管分类

针对纺织厂实际生产中采用人工分类纱管费时费力不够智能化等问题,提出了基于改进深度卷积神经网络的分类方法。先基于AlexNet模型框架对原有网络结构进行改进,卷积层全部使用3×3大小卷积核,且多个卷积核串联使用,提取物体更抽象高级特征。再融合滑动平均、L2正则化等方法提升泛化能力,采用L_ReLU激活函数避免部分神经元出现"死亡"现象。最后将检测样本输入训练好的神经网络,实现纱管分类。实验结果表明:该方法的识别率达到88.2%,较传统分类方法识别率提升15个百分点左右,相比于其他神经网络模型具有识别率高、所需时间短的优点,满足实际工业需求。

Classification of Bobbins Based on Improved Deep Neural Network

Herein,a classification method based on an improved deep convolutional neural network is proposed to address the problem that the artificial classification of bobbins is time-consuming,labor-intensive,and not sufficiently intelligent in the actual production of textile mills.First,the original network structure was improved based on the AlexNet neural network model framework.All convolutional layers used 3×3 size convolution kernels and multiple convolution kernels in series to extract more abstract features of objects.Next,we reintegrated the sliding average,conduct L2 regularization,and used other tricks to improve the generalization ability.Moreover,the L_ReLU activation function was used to avoid the "death"phenomenon of some neurons.Consequently,the test samples were input into the trained neural network to achieve the classification of bobbins.Experimental results show that the recognition rate of the method is 88.2%,which is approximately 15 percentage points higher than that by the traditional classification method.Compared with other neural network models,the proposed method demonstrates the advantages of high recognition rate and short time,which complies with the actual industrial requirements.