目的 针对传统无纺布缺陷分类检测中人工依赖性强、效率低等问题，提出一种能够满足工厂要求的卷积神经网络分类检测方法。方法 首先建立包括脏点、褶皱、断裂、缺纱和无缺陷等5种共计7万张无纺布图像样本库，其次构造一个具有不同神经元个数的卷积层和池化层的神经网络，然后采用反向传播算法逐层更新权值，通过梯度下降法最小化损失函数，最后利用Softmax分类器实现无纺布的缺陷分类检测。结果 构建了12层的卷积神经网络，通过2万张样本进行测试实验，无缺陷样本准确率可以达到100%，缺陷样本分类准确率均在95%以上，检测时间在35 ms以内。结论 该方法能够满足工业生产线中对于无纺布缺陷实时分类检测的要求。

The work aims to propose a convolutional neural network classification and detection method which can meet the factory requirements, for the purpose of solving the problem of high manual dependence and low efficiency in traditional non-woven fabric defect classification and detection. Firstly, a total of 70 000 images of five types of non-woven fabrics including dirty spot, fold, fracture, missing yarn and no defect were established. Secondly, a neural network of the convolutional layer and pooling layer with a number of different neurons was constructed. Then, the back propagation algorithm was used to update weights layer by layer, and the loss function was minimized by gradient descent method. Finally, Softmax classifier was used to realize defect classification and detection of non-woven fabric. A 12-layer convolutional neural network was constructed and tested with 20 000 samples. The accuracy of samples without defects could reach 100%, the classification accuracy of defect samples was above 95%, and the detection time was within 35 ms. The proposed method can meet the requirement of real-time classification and detection of non-woven fabric defects in industrial production lines.