Research On The Detection Of Porosity Of Concrete Aggregate

With the rapid economic development of society,concrete is widely used in real life in various fields such as road and bridge construction,building,decoration and so on.So it puts forward higher requirements to the performance index of concrete.However,there are many factors that may affect the performance of concrete,such as the water consumption,water cement ratio,sediment concentration,cement type,aggregate condition and admixture of concrete.Nowadays,the laggard equipment and methods are still being commonly used in the mixing station.The matching processes rely on experience completely rather than on the requirements of the object.This leads to the waste of raw materials,lower standards of concrete products,et cetera.This thesis presents a system that can perform the function of segmentation and porosity calculation using concrete aggregate images.After the sand and gravel material of concrete is stirred uniformly in the mixing chamber,the image is acquired by the CCD camera.Based on the MATLAB simulation platform,digital image processing technology combining the improved 2D Otsu threshold segmentation with regional growth phase is used to segment the data to obtain porosity and other data.The main work is as follows:Firstly,determine the research plan of porosity detection and use digital image processing technology to analyze the pores in the concrete aggregate.Taking into account the on-site environment,the CCD camera was used to capture images,and the Zhang Zhengyou calibration method is selected as the image calibration method.The image processing scheme is analyzed to determine the image preprocessing and segmentation scheme.Secondly,image processing technology is used to preprocess the collected particle images.Above all,grayscale image processing is done.And then the mean filter,median filter and gradient reciprocal weighting filter method are compared and analyzed.Median filter is selected as a most appropriate method of filtering.The image equalization following is to enhance the image contrast.During the process of image binarization,iterative threshold segmentation and maximum variance between classes are used to perform threshold segmentation and compared.Finally,area filling algorithm is used to further optimize the binary image.Thirdly,segment concrete aggregate images by the method combining an improved two-dimensional Otsu threshold segmentation algorithm with a regional growth algorithm.First of all,the initial segmentation of the preprocessed image is completed by the improved two-dimensional Otsu threshold segmentation algorithm,then the seed points are selected from the binary image.Next,the homogenous pixel regions are grouped together to grow a larger region according to the growth criterion.Repeat the previous iteration to complete the final segmentation.Lastly,Zhang Zhengyou calibration method is used to obtain the internal and external parameters of the camera.At the same time,the relationship between the camera coordinate system and the world coordinate system can be obtained.Basing on that,the ratio between the image pixel and actual porosity size can be calculated.Then,the porosity is known according to the segmentation results,and the characteristic parameters of the pores are analyzed.Through the research of this paper,the algorithm based on the combination of improved two-dimensional Otsu threshold segmentation and regional growth is used to segment the concrete aggregate image,and the image porosity information can be obtained real-timely.The method proposed in this topic can be modified by large amount of experiments.Applying the improved method to practical production will greatly increase the efficiency and performance of concrete production.It is also good for the economic and social benefits of users.