基于改进UNet网络的机制砂石粉分割量化方法

Segmentation and Quantification Method of Machine-made Sand Powder Based on Improved UNet Network

机制砂是机制砂混凝土的细骨料,其质量优劣对机制砂混凝土的强度、工作性、耐久性等性能影响十分显著,而其石粉含量决定着机制砂的质量优劣.由于传统的石粉检测方法程序存在繁琐、时间久、准确率低且难以量化等难题,本文提出了一种针对机制砂特征的改进型UNet网络的机制砂石粉分割量化方法.首先利用光学显微镜设备对机制砂颗粒进行图像采集,并使用对比度增强、查找表算法、低通滤波等对图像进行增强、去噪等预处理,然后引入深度残差和注意力机制模块,构建改进UNet网络模型,最终实现对机制砂中石粉部分的分割及量化计算.结果表明:本文构建的深度神经网络在机制砂训练集和验证集上的分割准确率高达95.2%和95.94%,且在相同数据上,相比UNet、FCN、Res-UNet方法,分割效果提升显著.

Machine-made sand is the fine aggregate for machine-made sand concrete.The quality of machine-made sand,determined by the stone powder content,has a significant impact on the strength,workability,durability,and other performance of machine-made sand concrete.Considering that with low accuracy and long duration,the traditional stone powder detection methods are cumbersome and difficult to quantify,this study proposes an improved UNet model based on the characteristics of machine-made sand.First,optical microscope equipment is used to collect images of machinemade sand particles,and these images are preprocessed by means of contrast enhancement,the look-up table algorithm,low-pass filtering,etc.Then,the deep residual and attention mechanism module is introduced to build an improved UNet model.Finally,segmentation and quantitative calculation are conducted on the stone powder in machine-made sand.The results show that the segmentation accuracy of the deep neural network constructed in this paper on the machine-made sand training dataset and the verification dataset is as high as 95.2%and 95.94%,respectively,and compared to the UNet,FCN,and Res-UNet methods,this method has significantly improved the segmentation effect on the same dataset.