南京信息工程大学学报2024，Vol.16Issue(1)：20-29,10.DOI:10.13878/j.cnki.jnuist.20230424004
基于优化DeepLabv3+的智能化高速铁路安全区域划分算法研究
Intelligent high-speed railway safety zone division based on optimized DeepLabv3+
摘要
Abstract
To address the problem that the railway safety zone division along the electrified railway with complex background needs to use actual fixed standard parts as reference and the division range is small,a smart safety zone division approach independent of reference objects is proposed.The GSD(Ground Sample Distance)parameters are calculated from relevant parameters in images collected by UAVs(Unmanned Aerial Vehicles),and the DeepLabv3+model with ECA-Net module is used to accurately segment the railway in the image.Then,a series of image process-ing operations such as edge detection,opening operation,and probability Hough transform are used to extract the key pixel points that make up the railway,and the least squares algorithm is used to fit the railway and obtain its mathe-matical expression.Finally,mathematical models,GSD parameters,and the mathematical expression of the railway are combined to complete the safety zone division.Experimental results show that the proposed approach achieves measurement accuracy over 90%,doesn't need to select fixed reference objects,and has strong adaptability and high robustness.The high practicality and reliability of the proposed approach provides effective technical support for safety management along the electrified railway.关键词
无人机/地面采样间距/DeepLabv3+/ECA-Net/安全区域Key words
unmanned aerial vehicle(UAV)/ground sample distance(GSD)/DeepLabv3+/ECA-Net/safety zone分类
信息技术与安全科学引用本文复制引用
王勇达,王硕禾,朱钰,常宇健,蔡承才,赵瑞康..基于优化DeepLabv3+的智能化高速铁路安全区域划分算法研究[J].南京信息工程大学学报,2024,16(1):20-29,10.基金项目
国家自然科学基金(12072205) （12072205）
河北省自然科学基金(A2022210024) （A2022210024）
中国铁路北京局集团有限公司科技研究开发计划(2020AGD02) （2020AGD02）
石家庄铁道大学研究生创新资助项目(YC2023027) （YC2023027）
