Construction Method of Fabric Defect Recognition System Based on Lightweight Convolutional Neural Network

Abstract

The present invention proposes a construction method of a fabric defect recognition system based on a lightweight convolutional neural network, the steps of which are as follows: firstly, configure the operating environment of the fabric defect recognition system, and obtain lightweight convolutions according to factorable convolutions Then, collect fabric image sample data, and standardize the fabric image sample data, the standardized fabric image sample data is divided into training image set and test image set, and then use the training strategy of asynchronous gradient descent to train the image set Input the lightweight convolutional neural network for training to obtain the LZFNet-Fast model. Finally, input the test image set into the LZFNet-Fast model for testing to verify the performance of the LZFNet-Fast model. The invention uses a factorable convolution structure to replace the standard convolution layer, effectively recognizes colored fabrics with complex textures, reduces the number of parameters and calculations of the model, and greatly improves the recognition efficiency.

Description

technical field [0001] The invention relates to the technical field of textile image recognition, in particular to a method for building a fabric defect recognition system based on a lightweight convolutional neural network. Background technique [0002] At present, the production cost of finished textile products is often affected by the original gray fabric, and most of the quality problems in the garment industry are related to fabric defects, which is one of the main problems faced by the textile industry. Fabric defects, often called fabric defects, refer to defects in the appearance of products caused by various unfavorable factors during the weaving process of cloth. From fiber raw materials to finished fabrics, it generally needs to go through multiple processes such as spinning, weaving, printing and dyeing, and defects may occur in each processing link. However, the cost of manual detection of fabric defects is too high, and it is easy to cause missed detection. ...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the technical problems of complex structure and large amount of calculation in the traditional deep convolutional neural network, the present invention proposes a fabric defect recognition method based on a lightweight convolutional neural network, and constructs a dedicated convolution module in the field of fabric recognition. This module incorporates the advanced factorable decomposed convolution structure. Firstly, the fabric image is extended to 32 dimensions through three-dimensional convolution operation as the input of the convolution module, and then the factorized convolutional layer is used for spatial filtering, and then the feature The graph is compressed into a low-dimensional space by a global average pooling layer on top of the network

Images