Surface defect recognition of chemical fiber yarn packages based on improved convolutional neural network

Focusing on the disadvantages of traditional manual method for defect detection of chemical fiber yarn packages, an improved convolutional neural network was proposed to classify and recognize the normal and three common defective yarn packages. The images of collected yarn package were processed into blocks before features were extracted by using the improved convolutional neural network. The global maximum pooling layer was used instead of the full connection layer, enhancing the robustness of images to spatial translations and reducing the model parameters. Softmax classifier was employed for classification. As a result, an active learning method was proposed for network learning. Firstly, a small number of labeled samples were used to train the network, and then the most valuable samples for improving network performance were selected and labeled, which were then added to the training samples. The experimental results show that this method can effectively facilitate the defect recognition of yarn packages, achieving a recognition accuracy of 97.1%. This method effectively reduces the number of labeled samples required by the network and saves a lot of labeling costs, with a certain degree of universality. Copyright No content may be reproduced or abridged without authorization.