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+

针对目前电气化铁路沿线复杂背景下铁路安全区域划分均需采用实际固定标准件为参照物且区域划分范围小等问题,提出一种无需参照物的高速铁路安全区域划分算法.首先基于无人机所采集图像中的相关参数计算出相应的GSD(地面采样间距)参数,然后利用加入ECA-Net模块的DeepLabv3+模型对图像中的轨道进行精确分割.通过边缘检测、开运算、概率霍夫变换等一系列图像处理操作,提取出构成轨道的关键像素点,并运用最小二乘法进行轨道拟合,得出轨道数学表达式.最后,结合数学算法和GSD参数以及轨道数学表达式,完成安全区域的划分.实验结果表明,所提算法测量精度高达90%以上,无需选取固定参照物,适应性强、鲁棒性高,具有较高的实用性和可靠性.

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 processing 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 mathematical 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.