Cloth defect classification method based on LBP features and convolutional neural network

Abstract

The invention discloses a cloth defect classification method based on LBP features and a convolutional neural network, and belongs to a defect classification method. According to the method, collectedsample images are randomly divided into a training set, a verification set and a test set in proportion; image preprocessing is carried out, weight fusion is carried out on the optimal feature vectorautomatically extracted from the sample image by using the convolutional neural model and the LBP feature vector calculated by the image; a final support vector is provided for the support vector machine classification model; the support vector machine optimizes hyper-parameters in the model by using a magnetotactic bacterium algorithm with an elitist strategy; the LBP features of the image are considered; after extraction, only weight addition of the feature vectors automatically extracted by the convolutional neural network needs to be carried out, interference of the cloth background is obviously reduced, the method has no requirement for the design and color of the cloth, the method can be used for solving the cloth defect detection problem of different designs and colors and single colors, and the method has the advantages of simple algorithm implementation, high classification accuracy and high operation efficiency.

Description

technical field [0001] The invention relates to a defect classification method, in particular to a cloth defect classification method based on LBP features and a convolutional neural network. Background technique [0002] Artificial intelligence is a national strategic emerging industry. With the continuous improvement of the information construction of the manufacturing industry, and the relatively complete industrial layout, and the textile industry is a traditional light industry pillar industry in my country, the final grading of textiles depends on the quality of the cloth. Cloth defect inspection is an important link in the production and quality management of the textile industry, and cloth defect intelligent detection is a technical bottleneck that has plagued the industry for many years. At present, almost all inspections are done manually, which is easily affected by subjective factors and lacks consistency; and the long-term work of inspectors under strong light ...

AI Technical Summary

Problems solved by technology

The detection efficiency and detection accuracy of the traditional detection algorithm are not high, and under the conditions of complex background and uneven illumination, the traditional detection algorithm has a high false detection rate and missed detection rate

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