高速公路路面养护辅助决策系统

针对高速公路养护部门的实际需要,设计基于地理信息系统(Geographical Information System,GIS)的高速公路路面养护辅助决策系统的体系结构,提出利用专家知识库实现辅助决策的概念模型,构建关键模型,给出系统开发方法和关键功能的实现过程,为管理人员提供科学的路面养护维修决策支持.     

基于激光雷达技术的路面平整度算法

平整度是路面运营养护状况评价的重要指标,传统路面平整度指标检测受人为因素影响大,检测效率低、成本高。基于激光雷达技术动态采集路面空间深度信息,通过选择合适的计算区域、加权滑动窗口滤波、采集设备的倾角消除等方法对数据进行分析处理,实现路面平整度标准差和国际平整度指数的快速计算。结果表明,基于激光雷达技术的路面平整度算法可将误差控制在合理范围之内,能够满足路面运营养护的要求,实现对公路路面平整度状况的智能评估。     

基于关联规则挖掘的路面损坏状况影响因素分析

本文基于路面评价指标中车辙深度指数和行驶质量指数来评价路面的损坏情况,使用关联规则挖掘环境、交通、路面等影响因素与路面状况之间的关联程度.针对关联规则Apriori算法复杂度和耗时的缺点,提出一种不生成候选集的方法来产生频繁集的改进Apriori算法,并通过实验对比证明改进的Apriori算法能够有效提升速度和性能.使用改进的Apriori算法分析路面评价指标及其影响因素之间的强关联规则,得到不同环境路面损坏的主要成因.本文结论能够对路面养护提供科学可靠的支持,可为路面养护部门提供合理的养护建议与数据支撑.     

公路的GIS养护之道

以GIS技术为基础,结合三维技术、无线通讯技术、GPS技术等,实现基于GIS的高速公路养护管理信息系统,能够更好地为养护决策服务。日益加大的交通压力对高速公路的养护管理提出新的问题。如何通过信息化手段提高高速公路养护水平,保障高速公路的道路通行能力是对高速公路养护者提     

深度学习在花卉养护技术中的应用

针对花卉养护过程中,由于人们缺乏专业的养护技术,花卉的养护难度大、成本高的问题,本文提出了一种基于深度学习的图像分类技术实现花卉的全自动化养护方法.因花卉的生长状况往往受诸多因素影响,仅依靠对花卉生长状况图像的分类容易对花卉的生长状况作出错误的判断,因此该方法设计了一种由花卉图像特征和生长环境参数组成的两个输入通道的卷积神经网络实现花卉生长状态的自动识别.实验表明,该方法可以提高花卉生长状况的识别准确率,从而可提高花卉的自动化养护技术水平.     

铁路线路养护施工决策最优化设计

针对铁路线路特性进行铁路线路养护机械化施工决策的方法分析,利用多指标评价方法与线性规划的原理实现线路养护施工决策.以此为线路养护企业的管理者提供科学和决策依据。     

针对单张沥青路面图像中裂缝的高效语义级修复

原始无损路面图像对分析路面损伤演化细节及制定下一步养护方案具有重要意义,而实地采集中无法获取路面裂缝图像对应的初始状态.为了获取其对应的无损路面图像,本文提出了一种基于深度图像先验的无监督沥青路面裂缝图像修复算法,可实现对单张路面图像中裂缝的高效语义级修复.首先采用鲁棒主成分分析算法去除路面裂缝图像表面的竖状条纹噪声.随后,采用最大类间方差法及形态学处理得到裂缝区域的二进制掩码图像.最后,运用提出的深度图像先验修复算法对裂缝区域进行修复得到最终的无损路面图像.在自采集路面裂缝图像数据集上对所提方法进行了评估.实验结果表明,所提方法能够有效实现路面裂缝图像语义级修复,峰值信噪比和结构相似性较传统的方法有了明显提升,平均达到了43.382 3 dB和0.983 4,且兼具高速度.     

GIS在基于iPad终端的路面养护管理服务平台的应用

近年来我国高速公路迅猛发展,交通运输量不断增加,传统的路面管理系统越来越难满足日益增长的道路养护需求。随着GIS技术的广泛应用,GIS与路面管理系统的结合越来越受到人们的关注。依托GIS强大的空间数据存储、快速查询、空间定位及良好的图形图像交互表达功能,研发了基于GIS的iPad终端路面养护管理服务平台,期望在一定程度上解决我国现存路面养护系统中的问题。     

基于自适应灰色模型的路面使用性能预测

在灰色理论建模的基础上,通过采用预测的最新数据替代最老数据这种方式,建立了自适应灰色模型,并将其应用于路面使用性能预测。能够克服缺少历史数据的不足,同时,与以往的预测模型相比,更加准确,更加符合路面使用性能的变化规律。通过MATLAB编程实现,使得预测更加简便易行。     

基于北斗定位的无人机路面检测系统分析

本次设计研究的路面检测系统是基于无人机平台和图像处理技术，提出基于北斗定位（BDS）的道路巡检无人机系统的构建方法。通过地面控制无人机沿设定路线自主巡航并采集路面信息，在自主识别过程中完成相应功能，预期功能包括路面异物判断、路面堵塞情况上报、公路防护林失火检测。能够实现路面异物位置以及失火位置的定位、识别、检测，为后期高速公路路面养护提供检测数据和理论依据。     

基于改进U-net模型的路面裂缝智能识别

路面裂缝快速检测及响应是道路养护部门的一项重要工作,然而传统的裂缝检测方法耗时且准确度低。因此,本文基于改进后的U-net模型实现对路面裂缝精准地自动识别。结合Canny边缘检测、Otsu 阈值分割算法和人为干预手段研发一款半自动标注软件,用以实现路面裂缝的像素级标注。研究以路面2D激光图像为数据集,并在此基础上通过数据增强进行数据集样本扩充,从而构建模型训练原始样本库;在实验分析阶段,使用交叉熵损失函数判断预测值与真实值的误差大小,并结合Adam 算法优化模型。研究表明改进后的U-net模型在识别精度及泛化能力上均优于原U-net模型及全连接神经网络模型。该研究将为道路养护管理部门的路面病害快速检测提供技术支撑,从而利于快速响应、采取措施保证路面的行车安全。     

An intelligent neural-fuzzy model for an in-process surface roughness monitoring system in end milling operations

In this research, a new intelligent neural-fuzzy in-process surface roughness monitoring (INF-SRM) system for an end milling operation was developed. The success of the INF-SRM system depends on an accurate decision-making algorithm, which can analyze the input factors and then generate an accurate output. A new neural-fuzzy model was proposed and implemented as decision-making algorithm for the INF-SRM system. The objective of the new model is to achieve higher accuracy for surface roughness prediction and solve the disadvantages of both neural networks and fuzzy logic. The neural-assisted method was implemented to generate the fuzzy IF-THEN rules for the model. To evaluate the performance of the new neural-fuzzy model, a neural networks model was applied to develop another surface roughness monitoring system for comparison. A statistical method was finally employed to analyze the accuracy between these systems.     

多级卷积神经网络的沥青路面裂缝图像层次化筛选

如何快速准确地识别与评估沥青路面裂缝病害,已成为路面养护和保障道路安全的重要任务之 一。实际采集路面图像中往往存在大量的非裂缝图像,在保证裂缝图像无漏筛的前提下,尽可能提高裂缝图像 的精确率与非裂缝图像的真负例率,则对于降低人工筛选的工作强度,以及后续裂缝自动分割与病害损坏程度 评估具有重要实际意义。故此,提出了一种多级卷积神经网络的沥青路面裂缝图像筛选方法,由训练、微调与 验证三阶段构成,利用微调集获得 softmax 层输入微调增量。为避免裂缝图像召回率增加与精确率下降的问题, 在对比不同卷积神经网络筛除的非裂缝图像异同基础上,采用改进 AlexNet 作为一级筛选网络,VGG16 或 ResNet50 作为二、三级筛选网络的层次化处理模型。对于含噪声及复杂路面图像测试集的实验结果表明,三级 层次化筛选模型能在 100%召回裂缝图像时,达到高的真负例率及准确率。与其他方法的对比实验表明,所提 方法可有效解决沥青路面裂缝图像漏筛问题,且具有更好的检测效果。     

Prediction maintenance integrated decision-making approach supported by digital twin-driven cooperative awareness and interconnection framework

Predictive maintenance is one of the important technical means to guarantee and improve the normal industrial production. The existing bottlenecks for popularization and application are analyzed. In order to solve these problems, a cooperative awareness and interconnection framework across multiple organizations for total factors that affect prediction maintenance decision-making is discussed. Initially, the structure and operation mechanism of this framework are proposed. It is designed to support the sharing of data, knowledge and resources. As a key supporting technology, the digital twin is also integrated into it to improve the accuracy of fault diagnosis and prediction and support making a maintenance plan with higher accuracy and reliability. Then, under this framework, an integrated mathematical programming model is established with considering the parameter uncertainty and an NSGA-II hybrid algorithm is utilized to solve it. Moreover, an adjustment strategy for a maintenance plan is discussed in response to the dynamic characteristics of the actual maintenance environment. Finally, a case, prediction maintenance decision-making for bearings in grinding rolls of the large vertical mill, is studied. Analysis results verify the advantage of the integrated solving mechanism based on the proposed framework. The framework and integrated decision-making approach can guide the implementation of predictive maintenance with higher accuracy and reliability for industrial enterprises.     

An optimum feature extraction method based on Wavelet–Radon Transform and Dynamic Neural Network for pavement distress classification

Quantification of pavement crack data is one of the most important criteria in determining optimum pavement maintenance strategies. Recently, multi-resolution analysis such as wavelet decompositions provides very good multi-resolution analytical tools for different scales of pavement analysis and distresses classification. This paper present an automatic diagnosis system for detecting and classification pavement crack distress based on Wavelet–Radon Transform (WR) and Dynamic Neural Network (DNN) threshold selection. The algorithm of the proposed system consists of a combination of feature extraction using WR and classification using the neural network technique. The proposed WR + DNN system performance is compared with static neural network (SNN). In test stage; proposed method was applied to the pavement images database to evaluate the system performance. The correct classification rate (CCR) of proposed system is over 99%. This research demonstrated that the WR + DNN method can be used efficiently for fast automatic pavement distress detection and classification. The details of the image processing technique and the characteristic of system are also described in this paper.     

CrackTree: Automatic crack detection from pavement images

Pavement cracks are important information for evaluating the road condition and conducting the necessary road maintenance. In this paper, we develop CrackTree, a fully-automatic method to detect cracks from pavement images. In practice, crack detection is a very challenging problem because of (1) low contrast between cracks and the surrounding pavement, (2) intensity inhomogeneity along the cracks, and (3) possible shadows with similar intensity to the cracks. To address these problems, the proposed method consists of three steps. First, we develop a geodesic shadow-removal algorithm to remove the pavement shadows while preserving the cracks. Second, we build a crack probability map using tensor voting, which enhances the connection of the crack fragments with good proximity and curve continuity. Finally, we sample a set of crack seeds from the crack probability map, represent these seeds by a graph model, derive minimum spanning trees from this graph, and conduct recursive tree-edge pruning to identify desirable cracks. We evaluate the proposed method on a collection of 206 real pavement images and the experimental results show that the proposed method achieves a better performance than several existing methods.     

基于分形理论的三维路面谱重构及在多体动力学软件中的应用

合理的道路纹理特征可以更好地反映路面抗滑性能和轮胎/路面接触特性。基于分 形理论提出了一种三维路面谱重构方法。依据国标给出二维随机路面谱的时域表达,利用计盒 维数法计算各级路面分形维数,综合应用随机中点位移法和分形布朗运动原理将传统二维路谱 拓展为三维路面谱。以典型的减速带为例,将特殊激励同构到含有细节形貌的平整路面谱中。 在 TruckSim 软件中通过编译实现三维路面谱在车辆多体动力学软件中的应用。通过对比二维路 谱和三维路谱下车辆动力学响应发现：垂向力和纵向力差异不大,有较好地一致性,但侧向力 相差比较大,表明所建立的三维路面谱有较好精度的同时反映了路面的三维纹理特性,为车辆 曲线通过性能和车辆侧翻控制研究提供了基础。     

面向矢量路网的自适应紧凑二维图像表达方法

矢量路网的二维图像表达旨在建立道路到图像的转化关系，对于道路交通流预测等实际问题具有重要研究价值。针对目前研究存在的空间拓扑关系丢失和图像分辨率不易确定等问题，提出了一种矢量路网的自适应二维图像表达方法。该方法能够自适应不同路网结构，在最大维持矢量路网拓扑关系的前提下，将道路路段一一映射到像素单元上，从而生成矢量路网的紧凑二维图像。选取国内外数百个城市不同类型的矢量路网对算法的性能进行验证，通过与随机编码和顺序编码的结果进行对比，证明了该方法的有效性与合理性。     

Pothole detection in asphalt pavement images

Pavement condition assessment is essential when developing road network maintenance programs. In practice, the data collection process is to a large extent automated. However, pavement distress detection (cracks, potholes, etc.) is mostly performed manually, which is labor-intensive and time-consuming. Existing methods either rely on complete 3D surface reconstruction, which comes along with high equipment and computation costs, or make use of acceleration data, which can only provide preliminary and rough condition surveys. In this paper we present a method for automated pothole detection in asphalt pavement images. In the proposed method an image is first segmented into defect and non-defect regions using histogram shape-based thresholding. Based on the geometric properties of a defect region the potential pothole shape is approximated utilizing morphological thinning and elliptic regression. Subsequently, the texture inside a potential defect shape is extracted and compared with the texture of the surrounding non-defect pavement in order to determine if the region of interest represents an actual pothole. This methodology has been implemented in a MATLAB prototype, trained and tested on 120 pavement images. The results show that this method can detect potholes in asphalt pavement images with reasonable accuracy.