摘要
Abstract
The quality testing session of tiles based on manual sorting not only causes waste of human resources, but cannot guarantee the quality of detection accuracy, affecting the improved quality of wall and floor tiles. In order to save costs and further improve the production efficiency of wall and floor tiles, in this paper, the features of co-occurrence matrix under color channels is taken as image visual features, and by taking advantage of image texture and color information, a BP neural network that applies to defect classification of wall and floor tiles is trained. Through data analysis of experimental results, the wall and floor tile detection technology based on BP neural network can get a better test results for a variety of sizes, colors, patterns of wall and floor tiles.关键词
颜色通道/共生矩阵特征/墙地砖缺陷/BP神经网络Key words
color channels/the features of co-occurrence matrix/the defects of wall and floor tile/BP neural network分类
信息技术与安全科学
