摘要
针对现有铁路轨道检测识别算法的准确性和鲁棒性不高的问题,提出了一系列有效的图像处理技术来实现轨道线检测。首先,采用加权平均法对图像进行灰度化处理,以减少计算量并提高计算速度。接着,利用高斯滤波对处理后的灰度图像进行去噪处理,以消除噪声干扰,提高后续处理步骤的准确性。然后,应用Sobel算子边缘检测算法从图像中提取边缘信息,有效识别图像中的边缘特征,准确提取轨道线的边缘信息。最后,利用三次函数对提取出的边缘信息进行拟合,得到平滑的轨道线。通过实验证明,所提出的轨道线检测方法显著提高了检测成功率,在轨道线检测任务中表现出良好的性能,并具备准确性和鲁棒性。
In order to address the issue of low accuracy and robustness in existing railway track detection and recognition algorithms,a series of effective image processing techniques are proposed to achieve track line detection.Firstly,the image is grayscale using the weighted average method,reducing the computational load and improving speed.Next,Gaussian filtering is applied to denoise the processed grayscale image,eliminating noise interference and enhancing subsequent processing accuracy.The Sobel operator edge detection algorithm is then utilized to extract edge information,effectively identifying edge features and accurately extracting track line edges.Finally,the extracted edge information is fitted using a cubic function to obtain a smooth envelope line.Experimental verification demonstrates that the proposed track line detection method significantly improves detection success rate,exhibiting excellent performance and high accuracy and robustness.
作者
赵鸿亮
郭佑民
Zhao Hongliang;Guo Youmin(Institute of Mechanical and Electrical Technology,Lanzhou Jiaotong University,Lanzhou 730070,China)
出处
《机电工程技术》
2024年第5期162-166,共5页
Mechanical & Electrical Engineering Technology
基金
国家自然科学基金资助项目(72061021)。
关键词
机器视觉
边缘检测
灰度处理
弯轨拟合
machine vision
edge detection
grayscale processing
rail bending fit
