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Research on Color Uniformity and Seam Detection of Standard Test Paper Based on Machine Vision

机译:基于机器视觉的标准试验纸的色彩均匀性和接缝检测研究

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For the detection of product color uniformity and seam texture defects in the production process of test paper, detection methods are proposed respectively. For the detection of the color uniformity, a uniformity detection method based on dynamic light correction is proposed. The illumination correction image is used for color dynamic compensation of the test paper image to solve the influence of uneven illumination on the test paper. For the detection of texture defects such as seams, a texture defect detection method based on gray gradient aggregation intensity is proposed. This method uses one-way Gaussian convolution to strengthen features, and then calculates the gradient, divides the region and calculates the gradient extremum aggregation strength to judge texture defects, which can quickly and efficiently find seam defects. The experimental results show that the above methods have obvious effects on the color uniformity and seam detection of the test paper. The color uniformity accuracy rate is 96%, the seam detection accuracy rate is 97%. The total detection time of a single test paper is about 90ms. The algorithm is real-time and accurate, and can be used in actual production detection.
机译:为了检测试验纸的生产过程中的产品颜色均匀性和缝纹理缺陷,分别提出了检测方法。为了检测颜色均匀性,提出了一种基于动态光校正的均匀性检测方法。照明校正图像用于测试纸张图像的颜色动态补偿,以解决不均匀照明对试纸的影响。为了检测诸如接缝之类的纹理缺陷,提出了一种基于灰度梯度聚集强度的纹理缺陷检测方法。该方法使用单向高斯卷积来加强特征,然后计算梯度,划分区域并计算梯度极值聚集强度以判断纹理缺陷,这可以快速有效地找到接缝缺陷。实验结果表明,上述方法对试纸的颜色均匀性和接缝检测具有明显的影响。颜色均匀性精度率为96%,接缝检测精度率为97%。单个测试纸的总检测时间约为90ms。该算法是实时和准确的,可用于实际生产检测。

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