Convolutional neural network-based yarn dyed fabric defect detection method

Abstract

The invention discloses a convolutional neural network-based yarn dyed fabric defect detection method. The method comprises a training stage and a detection stage in total. In the training stage, firstly a yarn dyed fabric defect image library is established, and images are preprocessed for reducing the influence of noises and image textures; the images and image tags are packaged; and an AlexNet convolutional neural network-based yarn dyed fabric defect detection model is built; a series of operations of image convolution, pooling, batch normalization, full connection and the like are carried out; defect features in the images are extracted; and a convolution kernel number, a layer number, a network structure and the like of the network model are improved, so that the accuracy of predicting test images by the built convolutional neural network model is further improved. By using a deep learning method, the convolutional neural network model is built for detecting yarn dyed fabric image defects. Compared with a conventional method, a detection result is more accurate and the yarn dyed fabric defects can be detected more efficiently.

Description

Technical field [0001] The invention belongs to the technical field of deep learning and machine vision, and relates to a color fabric defect detection method based on a convolutional neural network. Background technique [0002] my country is a big country in the production and export of textiles. With the development and progress of science and technology, the textile industry is also facing fierce competition while prospering. Improving the quality of textiles is a key factor in improving the competitiveness of my country's textile industry. Defective textiles will affect sales and waste a lot of manpower and material resources. Therefore, early detection of defects will help improve product quality and enhance work efficiency in the production process. However, the current defect detection is done manually, which is easily interfered by human subjective factors, the detection efficiency is low, the investment is large, and the long-term continuous working has great damage to ...

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a color-dyed fabric defect detection method based on convolutional neural network, which solves the problems of low detection efficiency, easy to be affected by subjective factors, and labor-intensive and material-resource-consuming problems in the existing traditional artificial color-dyed fabric defect detection

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