College of Art and Design, Shaanxi University of Science and Technology, Xi'an, China.
College of Mechanical & Electrical Engineering, Shaanxi University of Science and Technology, Xi'an, China.
Comput Intell Neurosci. 2022 Jul 19;2022:5625945. doi: 10.1155/2022/5625945. eCollection 2022.
In order to solve the surface defects such as white silk, spots, and wrinkles on the fabrics in the process of digital printing production, a surface defect detection system for printed fabrics based on the accelerated robust feature algorithm is proposed. The image registration is mainly carried out by the speeded up robust features (SURF) algorithm; the bidirectional unique matching method is used to reduce the mismatch points, realize the accurate registration of the image, and extract the defect information through the difference algorithm. The experiment uses multiple images to verify the performance of the improved SURF algorithm. The experimental results show that the detection accuracy of the new system for surface defects of printed fabrics reaches 98%. The algorithm has higher detection rate and faster detection speed, which can meet the needs of practical industrial applications.
为了解决数码印花生产过程中织物表面的白丝、斑点和皱纹等缺陷问题,提出了一种基于加速稳健特征算法的印花织物表面缺陷检测系统。该系统主要通过加速稳健特征(SURF)算法进行图像配准;采用双向唯一匹配方法减少误匹配点,实现图像的精确配准,并通过差分算法提取缺陷信息。实验采用多幅图像验证改进后的 SURF 算法的性能。实验结果表明,该新系统对印花织物表面缺陷的检测准确率达到 98%。该算法具有更高的检测率和更快的检测速度,能够满足实际工业应用的需求。