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基于卷积神经网络的土体含水率智能识别
庞元恩, 王智诚, 李旭, 杜赛朝
PDF(3284 KB)
PDF(3284 KB)
基于卷积神经网络的土体含水率智能识别
Moisture Content Recognition Model of Unsaturated Soil Based on Convolutional Neural Networks
土体含水率是影响细粒土性质的主要因素.土体表层含水率的快速识别是农业和岩土工程中智能监测和智能建造技术发展中的急迫需求.为了克服传统含水率测量或监测方法无法满足土体表层含水率的实时无损监测的局限性，特研发基于图像的含水率智能识别算法.首先在实验室中收集了4种不同类别的土体、在不同含水率下的表面照片，获得了超过1 400张图片的高质量样本库，为机器学习模型构建奠定了数据基础.然后采用经典的卷积神经网络对土体含水率图像数据集进行学习，建立了土体含水率智能识别模型.模型比选结果表明：基于ResNet34架构的土体含水率识别模型效果最佳，在测试集上的含水率预测平均误差约为2%.该模型初步满足了土体表层含水率的实时无损监测需求，能够为农业和岩土工程中智能监测和智能建造技术发展提供重要手段.
The moisture content of the soil is the main factor affecting the quality of fine-grained soil. Rapid recognition of soil surface moisture content is an urgent need for developing intelligent monitoring and construction technology in agricultural and geotechnical engineering. In order to overcome the limitation that traditional water content measurement or monitoring methods cannot meet the real-time nondestructive monitoring of soil surface moisture content, an intelligent moisture content recognition algorithm based on the image is developed. Firstly, we collected surface photos of 4 different types of soils under different moisture contents in the laboratory and obtained a high-quality sample library of more than 1 400 pictures, which laid a data foundation for machine learning model construction. Then the classical convolutional neural network is used to learn the image dataset of soil moisture content, and the intelligent recognition model of soil moisture content is established. The model comparison results show that the model based on ResNet34 architecture has the best moisture content recognition effect, and the average error of moisture content prediction on the test set is about 2%. This model basically meets the requirement of real-time nondestructive monitoring of soil surface moisture content and can provide an essential means for the development of intelligent monitoring and construction technology in agricultural and geotechnical engineering.
土体含水率 / 深度学习 / 卷积神经网络 / 智能监测 / 智能建造 / 工程地质
soil moisture content / deep learning / convolutional neural network (CNN) / intelligent monitoring / intelligent construction / engineering geology
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