针对花岗岩含水率检测周期长、自动化程度低等问题,以白麻花岗岩为研究对象,提出了基于图像特征的含水率检测方法。利用数字图像处理技术对白麻花岗岩图像进行分割、亮度归一化、降噪等处理,以降低图像亮度不同和颜色对比度差的影响,分析了图像灰度直方图的均值、方差、歪斜度、峰态、熵等5种图像特征与含水率之间的相关性,基于最小二乘法进行线性回归分析,建立了基于图像特征的白麻花岗岩含水率检测模型。通过含水率检测试验对该模型进行验证,结果表明,检测相对误差的均值为2.10%,方差为1.05,平均检测时长为15.59 s,该模型具有较高的检测精度和较快的检测速度。

Aiming to solve the problems of long period and low automation of granite moisture content detection and taking white granite as the research object, a moisture content detection method based on image features was proposed. The digital image processing technology was used to segment, normalize and denoise the white granite image to reduce the influence of different image brightness and poor color contrast. The correlation between the five image features of image gray histogram and water content was analyzed, and the linear regression analysis is carried out based on the least square method, The moisture content detection model of white granite based on image features was established. The model was verified by moisture content detection test. The results show that the mean value of detection relative error is 2.10%, the variance is 1.05, and the average detection time is 15.59 s, indicating that the detection model has high detection accuracy and fast detection speed.