基于轻量级模型的隧道岩性快速识别方法

为了解决隧道岩性现有识别方法中识别时间长、安全性低、主观性大等问题,结合不同岩性表面具有不同的成分特征,提出了一种基于轻量级模型与岩石图像的隧道岩性快速识别方法.首先,通过相机采集隧道常见的片麻岩、花岗岩、石灰岩、大理岩、凝灰岩、砂岩等6类主要岩石,建立了岩石图像数据集并划分训练集、验证集与测试集;然后,基于轻量级模型...

Rapid Identification Method for Lithology of Tunnel Based on Lightweight Model

In order to solve the problems of long identification time, low security, and high subjectivity in the existing identification methods of tunnel lithology, given the fact that composition characteristics differ among lithological surfaces, a rapid identification method of tunnel lithology based on the lightweight model and rock images was proposed. First, six types of major rocks in tunnels, including gneiss, granite, limestone, marble, tuff and sandstone, were collected by camera, and the rock image data set was established and divided into training set, verification set and test set. Then, based on the lightweight model MobileNet V2, pre-training was conducted on the ImageNet data set, the structure of the model classifier was improved to adapt to the rock data set, and 1170 images of the training set were trained using the transfer learning method for model training to obtain the rock lithology recognition model. Finally, a total of 300 test set images were selected and tested offline, and compared with those of the VGG16 model and the SVM (support vector machine) model. The experimental results show that the overall evaluation indexes of the model on the test data set were above 85%, of which the evaluation indexes of tuff reached more than 94%, the size of the model was only 28.3 MB, and the average recognition time was 2880 ms, indicating that the recognition model was small in size, high in recognition accuracy, and fast in recognition time, which is superior to traditional methods in accuracy and recognition speed.