Research And Implementation Of Textile Defect Detection Algorithm

Quality,as a key factor in production,attracts much attention in all walks of life.As a large population country,China consumes a lot of textiles.In the textile industry,textile defect detection has become the lifeblood of production.Because the defects of textiles will seriously affect the subsequent production links,the defects in textiles will reduce the quality and price of the finished products,and the existence of serious defects will make the finished products become waste products.Therefore,it is urgent to avoid using defective textiles in the production and processing of finished products.At present,the detection of textile defects in our country depends on manual detection,which is not only time-consuming and laborious,but also has a large error.In recent years,with the rapid development of image processing technology,textile defect detection technology based on image processing technology has emerged.With the continuous development of image processing technology,its application field is also expanding.The research shows that using image processing technology to extract color,texture and other information in the region to be detected for target image detection can effectively improve the accuracy of image detection results.In this paper,textile defect detection and image processing technology are combined.The image processing technology is used to detect the textile pictures with defects.The convolution neural network model is used to compare and analyze the detection accuracy.The model selects densenet neural network based on deep learning and BP neural network based on deep learning.The two neural networks detect the textile pictures.The experimental data selects the textile pictures collected by the factory provided by the textile factory A large number of tests have been carried out.The comparison and analysis of the experimental results show that the results of densenet neural network in the detection of textile defects are better and the accuracy is higher than that of BP neural network.The network makes full use of the feature information extracted from each convolution layer,greatly reduces the number of parameters used,and is easy to train,convenient,fast and efficient.In the face of complex images,densenet network model still has a good retrieval effect,with the accuracy of 94.8%.In this paper,the algorithm of textile defect detection is realized,including image collection,defect detection and result release.In this paper,HTML5 + bootstrap + Java Script development language is used in the design of the host computer,and SSM + My SQL is used in the background to develop the algorithm of textile defect detection.In this paper,image processing technology is applied to textile defect detection,which provides a new idea for industrial textile detection.