基于实例分割和单目视觉的有轨电车障碍物时空距离计算方法  

Space-Time Distance Calculation Method of Tram Obstacle Based on Instance Segmentation and Monocular Vision

作　　者：黄世泽 秦晋哲 陶婷 董德存 沈拓 HUANG Shize;QIN Jinzhe;TAO Ting;DONG Decun;SHEN Tuo(Shanghai Key Laboratory of Rail Infrastructure Durability and System Safety,Shanghai 201804,China;Key Laboratory of Road and Traffic Engineering of Ministry of Education,Tongji University,Shanghai 201804,China;Cuizhu Sub-district Office,Luohu District,Shenzhen City,Shenzhen 518020,Guangdong,China;School of Optical-Electrical and Computer Engineering,University of Shanghai for Science and Technology,Shanghai 200093,China)

机构地区：[1]上海市轨道交通结构耐久与系统安全重点实验室,上海201804 [2]同济大学道路与交通工程教育部重点实验室,上海201804 [3]深圳市罗湖区翠竹街道办事处,广东深圳518020 [4]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《计算机工程》2025年第4期239-248,共10页Computer Engineering

基　　金：国家重点研发计划(2022YFB4300501);上海市科委课题(23DZ2204900)。

摘　　要：有轨电车因采用共享路权和司机目视行车的方式运行,与地铁、磁悬浮等城市轨道交通相比,更容易和入侵障碍物发生碰撞。因此,为了保障有轨电车的运行安全,提出一种基于实例分割和单目视觉的有轨电车障碍物时空距离计算方法。首先基于实例分割模型提取出障碍物和轨行区的轮廓点;然后基于单目视觉原理建立单目视觉测距模型,在引入有轨电车轨道标准轨距长度1.435 m作为先验知识后,实现在相机无标定情况下障碍物与列车的纵向距离计算;最后根据障碍物距离轨行区最近的点及对应的轨道端点计算障碍物与轨行区的横向距离。该方法通过引入有轨电车标准轨距长度作为先验知识计算障碍物的时空距离,填补了轨道交通领域障碍物时空距离计算研究的空白,并且通过引入实例分割模型,以像素级精度确定障碍物测距关键点,实现了障碍物时空距离的精准计算。通过从现实场景中拍摄的实验数据来验证所提方法的可行性,实验结果表明,该方法的纵向距离计算结果的最大正误差为1.60 m,最大负误差为1.05 m,距离计算结果具有较高的准确度。Trams,owing to their operation in a shared right-of-way and reliance on driver visual observation,are more prone to collisions with intruding obstacles than urban rail transit systems,such as subways and maglev trains.Therefore,to ensure the operational safety of trams,a method for calculating the space-time distance of obstacles based on instance segmentation and monocular vision is proposed.First,the contour points of the obstacles and track area are extracted using an instance segmentation model.Subsequently,a monocular vision ranging model is established based on the principles of monocular vision.By incorporating a standard gauge length of 1.435 m as prior knowledge,the longitudinal distance between the obstacles and tram is calculated without camera calibration.Finally,the lateral distance between the obstacle and track area is calculated based on the point on the obstacle closest to the track area and the corresponding track endpoint.This method fills a research gap in the field of rail transit by calculating the space-time distance of obstacles using the standard gauge length of trams as prior knowledge.Additionally,by introducing an instance segmentation model,the key points for obstacle distance measurement are determined with pixel-level accuracy,enabling the precise calculation of the space-time distance of obstacles.The feasibility of the proposed method is verified using experimental data captured in real-world scenarios.The results show that the maximum positive and negative errors of longitudinal distance calculation are 1.60 m and 1.05 m,respectively,indicating a high level of accuracy in the distance calculation results.

关 键 词：有轨电车 实例分割 单目视觉 测距模型 障碍物 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]