基于优化DeepLabv3+的智能化高速铁路安全区域划分算法研究OACSTPCD

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.