基于机器视觉的纺织面料瑕疵自动检测与分类系统OA

In the advancement of intelligent manufacturing within the textile printing and dyeing industry,automation upgrade of fabric quality inspection has become a critical step for enhancing production efficiency and improving product quality.This paper presents a system leveraging machine vision and deep learning technologies to achieve automated defect detection and classification in textile fabrics.A high-resolution line-scan imaging platform suitable for high-speed production lines was designed,employing a multi-angle combined light source configuration to overcome imaging challenges posed by the complex textures of dyed and finished fabrics.At the algorithmic level,an enhanced convolutional neural network model performs deep feature extraction on captured images.Precise segmentation of defect areas is achieved by taking texture suppression algorithms..Additionally,a sample augmentation strategy overcomes classification accuracy limitations caused by imbalanced defect sample data in industrial settings.