Research on brocade defect detection algorithm based on deep learning

Abstract

The brocade weaving craft has a long history, with exquisite patterns and profound cultural connotations. It is an excellent representative of Chinese silk culture and an eye-catching business card in the intangible cultural heritage of mankind. The process of making brocade is a very complicated craft. In order to be able to detect defects in time during the production process, an improved SE-SSD fabric defect detection algorithm is proposed for the low efficiency of defect detection in traditional production, the large model affects the deployment and the shortcomings of DB-YOLOv3. By improving the network structure and optimizing the prior frame adjustment mechanism, the algorithm improves the ability of model feature extraction and greatly reduces the parameters and calculation of the network. The experimental results show that the SE-SSD algorithm effectively improves the missed detection of linear and weak target defects. Compared with the SSD network, the detection accuracy is increased by 27.55%, reaching 93.08% mAP, the detection speed is increased to 49FPS, and the network parameters are reduced. 51.5%, which improves the practicability of the algorithm, and the ability to detect small target defects still needs to be improved.