首页|基于车载视频抖动矢量的路面平整性评估方法

基于车载视频抖动矢量的路面平整性评估方法

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原文链接
  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
针对路面平整性评估流程繁琐、效率低、周期长等问题,提出基于车载视频抖动矢量的路面平整性评估方法,实现常态化场景下对路面状态的初步快速筛选评估.使用车载采集设备获取的行车视频作为评估数据基础,对车载图像进行预处理,增强行车视频图像的对比度,降低行车环境变化对视频图像对比度的影响.利用分块灰度投影算法对视频图像进行相似性判定,去除大偏差的抖动矢量和运动目标干扰,提取行车视频的主要抖动矢量特征.采用粒子群优化算法改进投影相关性曲线的搜索模式,通过使用行(列)方向的灰度投影曲线相关性作为适应度函数来提高算法的搜索效率.建立基于遗传算法(genetic algorithm,GA)优化的K-means聚类分析算法,实现了自主采集路段中不同车速条件下的路面平整性分级评估.通过自主采集数据实验验证,基于粒子群优化的灰度投影算法在检测平整路面时,耗时0.148 s,算法效率比原算法提高了91.41%;在检测粗糙路面时,耗时0.123 s,算法效率比原算法法提高了87.58%,且检测出的抖动矢量数值一致.本文提出的基于车载视频抖动矢量的GA-K-means路面平整性分级评估方法能够有效降低初始聚类中心的干扰.
Assessing Pavement Rougness Using Jitter Vector from In-vehicle Camera Videos
The process of assessing pavement smoothness is cumbersome,inefficient and time-consuming.To ad-dress these issues,a pavement smoothness assessment method based on in-vehicle video jitter vectors is proposed.This method enables preliminary and rapid screening of pavement conditions under normal scenarios.It uses driv-ing videos collected by onboard devices as the assessment data.Preprocessing enhances the contrast of driving vid-eo images and reduces the effect of changes in the driving environment on the contrast of video images.The video images then undergo block-wise grayscale projection and similarity determination to remove significant deviations in jitter vectors and interference from moving objects.This extracts the main jitter vectors from the driving videos.The particle swarm optimization(PSO)algorithm improves the search pattern of the projection correlation curve.Using the grayscale projection curve correlation formula as the fitness function in the row(or column)direction en-hances search efficiency of the algorithm.A genetic algorithm(GA)optimized K-means clustering algorithm is es-tablished to autonomously assess road smoothness at different vehicle speeds by combining vehicle speed and video jitter vectors.Experimental validation shows that the PSO-based grayscale projection algorithm detects smooth road surfaces in 0.148 s,improving efficiency by 91.41%compared to the original algorithm.For rough road surfaces,de-tection takes 0.123 s,improving efficiency by 87.58%,and consistently detects jitter vector values.The GA-K-means algorithm effectively reduces interference from initial cluster centers,avoiding premature conver-gence.

road engineeringjitter vector from in-vehicle camera videospavement smoothness assessinggray-scale projection algorithmGA-K-means algorithm

陈子昂、陈新、曾宇同、郭唐仪

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南京理工大学自动化学院 南京 210094

中国兵器工业集团北方光电股份有限公司 西安 710043

道路工程 车载视频抖动矢量 路面平整性评估 灰度投影法 GA-K-means聚类算法

国家重点研发计划-政府间国际科技创新合作项目南京市国际合作项目

2019YFE0123800202002013

2024

10.3963/j.jssn.1674-4861.2024.02.011

交通信息与安全
武汉理工大学 交通计算机应用信息网

交通信息与安全

CSTPCD北大核心
影响因子：0.598
ISSN：1674-4861
年,卷(期)：2024.42(2)
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