Image recognition method for fabrics defects based on improved Res-UNet network
The texture and color of complex patterned fabrics are often irregular,making it difficult to identify surface defects.To address the above issues,a textile surface defect image recognition method based on an improved Res UNet network was studied.Textile images were collected and processed with gray-scale,de-noising and histogram equalization.The bat algorithm was used to obtain the optimal extraction network layer number.The Res-UNet network was improved by increasing the feature extraction network layer number,and the improved Res-UNet network was used to identify textile surface defects to achieve accurate identification of textile surface defects.The results show that under the application of the proposed method,the identification coefficient of both plain color samples and color samples is above 0.9.Compared with the textile defect identification method based on label embedding method and the fabric defect detection method based on dual-channel high-resolution conversion network,the identification method based on the improved Res-UNet network can identify the contour coefficient of complex color samples better.The applicability is better,the recognition ability is stronger.
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