人民长江2024，Vol.55Issue(7)：59-64,6.DOI:10.16232/j.cnki.1001-4179.2024.07.008
基于Transformer语义分割模型的露天矿场识别
Open-pit mine recognition based on Transformer model
摘要
Abstract
Open-pit mine is an important object of water and soil conservation information supervision in production and con-struction projects.The efficient and accurate identification of its scope is of great significance for monitoring illegal mining behav-iors and strengthening the prevention and control of soil and water loss in the mining process.We introduced an intelligent recog-nition method utilizing a Transformer-based deep learning model for analyzing remote sensing images of open-pit mining areas.Comparative experiments were conducted on the open-pit mine dataset in Yibin City,Sichuan Province,using widely adopted deep learning recognition methods based on convolutional neural networks.The results indicated that the reveal precision,recall,F1-score,and IoU values of this method for identifying the scope of open-pit mines were 91.25%,90.66%,90.95%and 83.41%,respectively,which can meet the accuracy requirements of remote sensing supervision for water and soil conservation.Additionally,the efficiency and accuracy of our method remained superior to the contrasted methods while it shows equivalent run-ning efficiency,indicating significant practical utility.The method introduced in this paper holds substantial potential for wide-spread application,enabling swift and accurate recognition of open-pit mines across extensive regions.关键词
水土保持/遥感监管/露天矿场/深度学习/Transformer模型/语义分割/宜宾市Key words
water and soil conservation/remote sensing supervision/open-pit mine/deep learning/Transformer model/se-mantic segmentation/Yibin City分类
信息技术与安全科学引用本文复制引用
陈佳晟,游翔,沈盛彧,廖梓凯,张彤..基于Transformer语义分割模型的露天矿场识别[J].人民长江,2024,55(7):59-64,6.基金项目
国家自然科学基金项目(41601298) （41601298）
