全自动无人驾驶列车障碍物检测的轨道区域检测算法

Track Area Detection Algorithm for Obstacle Detection of Fully Automatic Driverless Train

摘要:

针对轨道交通全自动无人驾驶列车轨道障碍物检测领域中的轨道区域分割问题，提出一种基于视觉传感器的非接触式轨道区域检测算法。算法使用帧间差分阈值和灰度分布特征提取方法完成对视频图像数据的场景识别和标记；基于图像场景识别结果能够自适应调整参数输入的自适应边缘检测模块完成对图像的预处理和轨道轮廓的边缘检测；基于类滑动窗格的轨道区域边界搜寻模块构建滑动窗格搜索子模块和通带搜索子模块完成对轨道轮廓曲线的提取，最后使用卡尔曼滤波器提升检测结果的准确性和鲁棒性。实验结果表明该算法具备良好的检测性能。

Abstract:

A non-contact detection algorithm based on vision sensors is proposed to address the issue of track area segmentation in the context of fully automatic driverless trains for rail transit. The algorithm uses frame difference threshold and grayscale distribution feature extraction methods to perform scene recognition and labeling of video image data. Image preprocessing and edge detection of the track contour are completed by an adaptive edge detection module， which adjusts the parameter input based on the results of image scene recognition. The track area boundary search module consists of two submodules： the sliding pane search submodule and the passband search submodule， based on the sliding pane-like approach， in order to extract the track outline curve. Finally， a Kalman filter is used to improve the accuracy and robustness of the detection results. The experimental results show that the algorithm exhibits strong detection performance on track boundaries.