基于扩展Haar特征和DBSCAN的钢轨识别算法

Rail Area Extraction Using Extended Haar-like Features and DBSCAN Clustering

障碍物对列车的正常运营构成了极大的安全隐患,钢轨识别是实现障碍物检测的关键步骤。钢轨识别算法需要能够快速有效地检测列车前方钢轨的位置,同时不能占用过多的计算资源,影响障碍物检测程序的运行速度。为解决上述问题,文中提出一种基于扩展Haar特征提取和DBSCAN密度聚类的钢轨识别算法。首先通过仿射变换、池化、灰度均衡化、边缘检测等算法对图像进行预处理,然后基于扩展Haar特征提取图像中钢轨的特征点,最后利用DBSCAN算法对特征点进行聚类,提取出有效的特征数据点进行曲线拟合,从而识别钢轨的位置。通过车载实验结果表明,该方法能够在列车运行过程中有效检测到钢轨的位置,满足多场景、多工况的实际使用需求。

Obstacle is a potential threat to the normal operation of trains.Rail area extraction is a key step in the process of using the train’s forward-looking camera to detect obstacles.Rail area extraction algorithm needs to be able to quickly and effectively detect the position of the rail while not occupying too much computing resources to keep the normal calculation speed of the obstacle recognition algorithm.This paper proposes a rail area extraction algorithm based on extended Haar-like feature extraction and DBSCAN density clustering.Firstly,the image is preprocessed by algorithms such as affine transformation,pooling,gray level equalization,and edge detection.Then the feature points of the rail are extracted based on multiple extended Haar-like features.Finally,the DBSCAN algorithm is used to extract valid feature data points and curve fitting is performed through these points.The experimental result shows that the algorithm can effectively detect the position of the rail area during the running of the train,and meet the practical needs of multiple scenarios and conditions。