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A biomimicry approach to automation of road feature surveys

机译:一种仿生方法来自动化道路特征测量

摘要

Australia's road network is a very significant capital asset created by progressive investment over several decades, and is integral to the social and economic performance of the nation. Physical elements of the road infrastructure are numerous and include surfacing, bridges, lighting, signs and line-markings.udRoad condition assessment is critical to optimisation of both road performance and public investment strategies. Road condition data are of fundamental importance if a road owner is to demonstrate that best practice road management is being followed in line with investment and budgetary constraints. Road surveys that are able to quickly and efficiently identify features and assess their condition are therefore keystones of an effective road asset management system. Manual visual surveys are subjective and expensive, while digital imagery and off-the-shelf computer hardware and software packages have the potential to carry out image analysis in real time. This in turn has triggered a surge of interest in the possibility of automating road survey systems.udEfforts to date have stopped short of producing such survey systems. This research was motivated by a desire to understand why this is so, and to explore the way forward. The thesis therefore starts with a general review of the current condition of road assets in Australia, and the manual and semi-automated road condition survey systems widely used by asset managers. The key problems appear to be the difficulties in producing survey systems for quite different applications and survey conditions. Biomimicry suggests that a way forward might be to identify a generic system design, rather than seek to develop a single do-all survey system.udThe thesis then examines the suite of image processing and artificial intelligence tools, including expert systems and neural networks, that have become available to help automate such surveys. A generic design approach is proposed to develop automated road feature and condition survey systems, together with design application principles that are also largely inspired by biomimicry. The three principal system components are:ud1. Image acquisition, which mimics the work of the eyes.ud2. Image processing that parallels the work of the visual cortex.ud3. Feature recognition and condition assessment that mimic the cognitive aspects of a human brain, notably its biological neural networks.udAnalysis of the survey results, if necessary, constitutes a fourth system component. This often involves consideration of context and/or aggregation of information into one or more overall road condition indicators. Expert rule systems are ideally suited to such tasks.udA two-stage survey development process is proposed. First, a basic survey system is developed which performs well under ideal survey conditions, with the goal of understanding and establishing the fundamental aspects of the survey system. Next, an extended survey system is developed that can handle the more complex, non-ideal survey environments that must be addressed in an actual survey of a long stretch of rural highway. The extended survey system development process starts by examining the performance problems of a basic survey system applied to the more challenging survey conditions.udThe system design is illustrated by case studies of guidepost counting and centrelinemarking condition assessment. The satisfactory performance of the basic automated systems for these applications is demonstrated using typical sections of road. An extended system for guidepost counting is then produced and its performance examined on a survey of the Midland Highway in Tasmania. The thesis concludes by discussing how best to develop these automated systems into an operational tool through trials carried out in association with local government and road authorities.udCase studies are presented to demonstrate the basic survey system development process applied to guidepost counting and to evaluate centreline-marking condition. The approach to developing an extended survey system is demonstrated to produce an improved guidepost survey system able to deal with changing ambient light conditions. The satisfactory performance of this extended system under non-ideal conditions is confirmed via a 50 km field trial on a section of the national highway in Tasmania.
机译:澳大利亚的公路网是数十年间逐步投资创造的非常重要的资本资产,并且是该国社会和经济绩效不可或缺的一部分。道路基础设施的物理元素很多,包括铺面,桥梁,照明,标志和线条标记。 ud道路状况评估对于优化道路性能和公共投资策略至关重要。如果道路所有者要证明遵循最佳实践的道路管理符合投资和预算限制,则道路状况数据至关重要。因此,能够快速有效地识别特征并评估其状况的道路勘测是有效道路资产管理系统的重点。手动视觉调查是主观且昂贵的,而数字图像以及现成的计算机硬件和软件包则具有实时进行图像分析的潜力。反过来,这引发了人们对道路测量系统自动化的兴趣激增。 ud迄今为止,人们一直在努力生产这种测量系统。这项研究的动机是希望了解为什么会这样,并探索前进的道路。因此,本文首先对澳大利亚道路资产的现状进行了综述,并介绍了资产管理者广泛使用的手动和半自动道路状况调查系统。关键问题似乎是在生产针对完全不同的应用和调查条件的调查系统时遇到的困难。仿生学建议前进的道路可能是确定通用系统设计,而不是寻求开发单一的全面调查系统。 ud然后本文研究了图像处理和人工智能工具套件,包括专家系统和神经网络,已经可以用来帮助使此类调查自动化的工具。提出了一种通用设计方法来开发自动道路特征和状况调查系统,以及在很大程度上受到仿生学启发的设计应用原理。系统的三个主要组成部分是: ud1。图像采集,模仿眼睛的工作。 ud2。图像处理与视觉皮层的工作并行。 ud3。模仿人脑认知方面的特征识别和条件评估,尤其是其生物神经网络。 ud对调查结果的分析(如有必要)构成了系统的第四部分。这通常涉及考虑上下文和/或将信息汇总到一个或多个总体路况指标中。专家规则系统非常适合此类任务。 ud提出了一个两阶段的调查开发过程。首先,开发了一种基本的调查系统,该系统在理想的调查条件下表现良好,其目的是理解和建立调查系统的基本方面。接下来,将开发一个扩展的调查系统,该系统可以处理更复杂的,非理想的调查环境,而这对于长距离农村公路的实际调查必须解决。扩展的调查系统开发过程首先要研究适用于更具挑战性的调查条件的基本调查系统的性能问题。 ud通过路标计数和中心线标记条件评估的案例研究来说明系统设计。基本的自动化系统在这些应用中的令人满意的性能已通过典型的路段得到了证明。然后,生产了扩展的路标计数系统,并通过对塔斯马尼亚州Midland公路的一次调查来检查其性能。本文的结尾是讨论如何通过与地方政府和道路当局联合进行的试验来最好地将这些自动化系统开发为可操作的工具。 udCase研究旨在说明用于路标计数和评估中心线的基本调查系统开发过程标记条件。事实证明,开发扩展的调查系统的方法可以产生一种改进的路标调查系统,该系统能够应对不断变化的环境光条件。该扩展系统在非理想条件下的令人满意的性能已通过塔斯马尼亚州某条国家高速公路的50公里现场试验得到证实。

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