摘要
Abstract
To address the high cost of online detection equipment and the low adaptability and accuracy of online detection models that are caused by uneven lighting,high noise,low contrast and so on,a block-based template matching method incorporating fabric texture characteristics is proposed.Firstly,the template image set is evenly divided into N groups of sub-templates at the same positions to mitigate the effects of image illumination,reduce the model computation,and enhance the detection speed,with all image blocks being preprocessed.Then,the feature value information is extracted from the processed set of sub-templates at the same position,extracting two gray-level co-occurrence matrix(GLCM)feature values for each image block.These two feature values are then fused to construct a matching template.The mean feature value of all image blocks at the same position is calculated and used as the threshold for template detection,enabling automatic selection of template thresholds for different positions.Finally,the feature values of the image blocks in the experimental set are traversed and matched with sub-templates at the same positions to obtain fabric defect detection results.The detection experiments are conducted on a platform that simulates a fabric weaving environment,using defective gray fabrics from a weaving factory as the detected objects.The outcomes demonstrate the efficacy of the proposed method in detecting defects in gray fabrics,the mitigation of the impact of uneven external lighting on detection outcomes,and the enhancement of detection accuracy and adaptability.关键词
疵点检测/灰度共生矩阵/模板匹配/坯布/特征提取/在线检测Key words
defect detection/gray-level co-occurrence matrix(GLCM)/template matching/gray fabric/feature extraction/online detection分类
信息技术与安全科学
