Objective evaluation model of sewing flatness based on deep belief network

Abstract

In view of the low accuracy of objective automatic evaluation of fabric sewing flatness, an objective evaluation model of fabric sewing flatness based on feature parameters and deep belief network (DBN) is proposed. Firstly, image preprocessing including image grayscale processing, median filter denoising, and histogram equalization enhancement was performed on the image of the fabric sewing sample to extract the texture features of the fabric, so as to obtain a fabric feature image with higher quality and facilitate subsequent processing. Then the gray level co-occurrence matrix was constructed, and the four key feature parameters of energy, entropy, contrast and correlation of fabric image were extracted at 0°, 45°, 90° and 135°. On this basis, an automatic evaluation model of fabric sewing flatness based on DBN was constructed, and the model was trained using sewing images. Last it was verified by the extracted fabric sewing images. The results show that the evaluation accuracy of the model reaches 98.74%. Compared with the two methods of multiple linear regression model and BP network model, the proposed evaluation method can effectively evaluate the flatness of sewing objectively and provide a theoretical basis for the quality control of fabric garment appearance.

Keywords

• sewing flatness
• characteristic parameters
• deep belief network
• gray level co-occurrence matrix
• objective evaluation