Now showing 1 - 10 of 20
  • Publication
    Inspection of Cycleways with DataCycle - Preliminary Results
    (Irish Transport Research Network, 2017-08-29) ; ; ;
    This paper presents the first inspection results using DataCycle, a first maintenance management system for cycleways in Ireland. DataCycle provides a system of inspection and assessment of cycleways, along with an inspection manual. As a first application of DataCycle, the inspection method and assessment is applied to two different cycling routes in Cork, Ireland. The first route is the Passage West to Rochestown cycle route, which has been developed along an old disused rail line and is in an area where cycling and walking for both leisure and commuting is possible. The second route is the Cork City to Ballincollig Cycleway. This is the main cyclist commuter route between Ballincollig and the city centre. It covers a distance of 6.4km. The cycle route is comprised of a segregated roadside cycleway and a shared footpath and cycle route in parts. The assessments compare and contrast these two distinct routes and demonstrates how a Cycleway Management System(CMS) in the form of DataCycle can be beneficial to managing this asset in a targeted manner. The ease of use, ability to align with existing maintenance management systems and the possibility of upscaling the system is highlighted. The results present typical outputs for some of the routes inspected implementing the cycleway management system with suggested intervention options. The work demonstrates how the developed system can be easily implemented for cycleways and encourages the use of such centralised maintenance system for cycleways throughout the country. A substantial inspection database for cycling facilities should allow for implementation of asset management methodologies, including cross-asset management formats.
  • Publication
    Damage Assessment of the Built Infrastructure using Smartphones
    (University College Dublin, 2018-08-30) ; ; ;
    The use of image-processing and machine-learning algorithms for road condition monitoring has attracted considerable interest in recent years. This surge in popularity has been propelled by advances in camera technology and the emergence of state-of-the-art deep learning techniques which have allowed inspectors to obtain high-quality imagery on a consistent basis, and then use efficient techniques to recognise road defects with credibility. A wide variety of road defect detection techniques have been proposed, however, the influence that different image acquisition devices have on the accuracy of defect detection has not been studied despite being a key component. The use of smartphone cameras as an inspection tool is of particular interest as they have become ubiquitous in recent times and the built-in cameras have progressed significantly. These cameras are now capable of producing perfectly acceptable images, yet they are still not well compared against established benchmarks. In this paper, the qualities of smartphones are explored and compared against a dedicated DSLR (Digital Single-Lens Reflex) camera. An experiment was designed that involved capturing video footage of a road surface using a smartphone (Samsung Galaxy S7) and a dedicated imaging device (Canon 600D DRSL). A deep learning based crack detection method was applied to imagery from the smartphone and the DRSL cameras and the performances levels were subsequently compared. The results indicate that smartphones are a viable, low-cost method for executing quick assessments of the road integrity. The evaluation of smartphone cameras also addresses the ongoing uncertainty around the level of performance that can be achieved using cheaper sensors for quantitative purposes. Such findings provide reassurance for inspectors wishing to use their smartphones for simple monitoring tasks.
  • Publication
    A Cycle Route Planner Mobile-App for Dublin City
    (Irish Transport Research Network, 2012-02-28) ; ; ;
    In a road network, cyclists are the group exposed to the maximum amount of risk. Route choice of a cyclist is often based on level of expertise, perceived or actual road risks, personal decisions, weather conditions and a number of other factors. Consequently, cycling tends to be the only significant travel mode where optimised route choice is not based on least-path or least-time. This paper presents an Android platform based mobile-app for personalised route planning of cyclists in Dublin. The mobile-app, apart from its immediate advantage to the cyclists, acts as the departure point for a number of research projects and aids in establishing some critical calibration values for the cycling network in Dublin.
  • Publication
    Health impacts of cycling in Dublin on individual cyclists and on the local population
    There is an emerging consensus that personal and societal health benefits in cycling largely outweigh the risks. However, there exists limited research into the health impacts experienced by individuals who take up cycling or the marginal societal benefits resulting from incremental uptake of cycling. This paper models and estimates the health impacts of individuals in Dublin taking up cycling. The paper utilizes the 2011 census data of Ireland and a Burden of Disease (BOD) approach is used to estimate health impacts on the individuals taking up cycling for their regular commute and on the rest of the local population separately. The health impact to an individual changing from private car to cycling ranged from a benefit of 0.033 Disability Adjusted Life Years (DALYs)/year to a loss of 0.003 DALYs/year. The marginal health impact to the local population ranged from no change to a benefit of 0.006 DALYs/year. Increases in cycling have a consistently positive impact on the health of the local population, regardless of the current modal split. The net expected health impacts to the individual cyclists are also positive in most cases. However, for some individuals in the 20–29 age group, the expected health impact may be small to negative, mainly due to a higher traffic collision risk. Where total impacts of scenarios are modelled the potential negative health impacts to some individuals may be masked by the overall positive health benefits of cycling to the local population. When promoting cycling as an alternative to driving to improve population health impacts, the risks to some cyclists should be managed and mitigated through safe road systems approaches.
      367Scopus© Citations 14
  • Publication
    An underwater lighting and turbidity image repository for analysing the performance of image-based non-destructive techniques
    Image processing-based methods, capable of detecting and quantifying cracks, surface defects or recovering 3D shape information are increasingly being recognised as a valuable tool for inspecting underwater structures. It is of great practical importance for inspectors to know the effectiveness of such techniques when applied in conditions. This paper considers an underwater environment characterised by poor visibility chiefly governed by the lighting and turbidity levels, along with a range of geometry and damage conditions of calibrated specimens. The paper addresses the relationship between underwater visibility and the performance of image-based methods through the development and calibration of a first open-source underwater lighting and turbidity image repository (ULTIR). ULTIR contains a large collection of images of submerged specimens that have been photographed under controlled lighting and turbidity levels featuring various forms of geometry and damage. ULTIR aims to facilitate inspectors when rationalising the use of image processing methods as part of an underwater inspection campaign and to enable researchers to efficiently evaluate the performance of image-based methods under realistic operating conditions. Stakeholders in underwater infrastructure can benefit through this first large, standardised, well-annotated, and freely available database of images and associated metadata.
      828Scopus© Citations 31
  • Publication
    Short-term forecasting of bicycle traffic using structural time series models
    Short term forecasting algorithms are widely used for prediction of vehicular traffic flows for adaptive traffic management. However, despite the increasing interest in the promotion of cycling in cities, little research has been carried out into the use of traffic forecasting algorithms for bicycle traffic. Structural time series models allow the various components of a time series such as level, seasonal and regression effects to be modelled separately to allow analysis of previous trends and forecasting. In this paper, a case study at a segregated bicycle lane in Dublin, Ireland was performed to test the forecasting accuracy of structural time series models applied to continuous observations of cyclist traffic volumes. It has been shown that the proposed models can produce accurate peak period forecasts of cyclist traffic volumes at both 1 hour and fifteen minute resolution and that the percentage errors are lower for hourly forecasts. The inclusion of weather metrics as explanatory variables had varying effects on the forecasting accuracies of the models. These results directly aid the design of traffic signal control systems accommodating cyclists.
      335Scopus© Citations 8
  • Publication
    Regionally enhanced multiphase segmentation technique for damaged surfaces
    Imaging‐based damage detection techniques are increasingly being utilized alongside traditional visual inspection methods to provide owners/operators of infrastructure with an efficient source of quantitative information for ensuring their continued safe and economic operation. However, there exists scope for significant development of improved damage detection algorithms that can characterize features of interest in challenging scenes with credibility. This article presents a new regionally enhanced multiphase segmentation (REMPS) technique that is designed to detect a broad range of damage forms on the surface of civil infrastructure. The technique is successfully applied to a corroding infrastructure component in a harbour facility. REMPS integrates spatial and pixel relationships to identify, classify, and quantify the area of damaged regions to a high degree of accuracy. The image of interest is preprocessed through a contrast enhancement and color reduction scheme. Features in the image are then identified using a Sobel edge detector, followed by subsequent classification using a clustering‐based filtering technique. Finally, support vector machines are used to classify pixels which are locally supplemented onto damaged regions to improve their size and shape characteristics. The performance of REMPS in different color spaces is investigated for best detection on the basis of receiver operating characteristics curves. The superiority of REMPS over existing segmentation approaches is demonstrated, in particular when considering high dynamic range imagery. It is shown that REMPS easily extends beyond the application presented and may be considered an effective and versatile standalone segmentation technique.
    Scopus© Citations 52  593
  • Publication
    A Stereo-Matching Technique for Recovering 3D Information from Underwater Inspection Imagery
    Underwater inspections stand to gain from using stereo imaging systems to collect three-dimensional measurements. Although many stereo-matching algorithms have been devised to solve the correspondence problem, that is, find the same points in multiple images, these algorithms often perform poorly when applied to images of underwater scenes due to the poor visibility and the complex underwater light field. This article presents a new stereo-matching algorithm, called PaLPaBEL (Pyramidal Loopy Propagated BELief) that is designed to operate on challenging imagery. At its core, PaLPaBEL is a semiglobal method based on a loopy belief propagation message passing algorithm applied on a Markov random field. A pyramidal scheme is adopted that enables wide disparity ranges and high-resolution images to be handled efficiently. For performance evaluation, PaLPaBEL is applied to underwater stereo images captured under various visibility conditions in a laboratory setting, and to synthetic imagery created in a virtual underwater environment. The technique is also demonstrated on stereo images obtained from a real-world inspection. The successful results indicate that PaLPaBEL is well suited for underwater application and has value as a tool for the cost-effective inspection of marine structures.
      790Scopus© Citations 22
  • Publication
    High dynamic range image processing for non-destructive-testing
    (Taylor & Francis, 2011-10-17) ; ;
    This paper proposes High Dynamic Range (HDR) imaging as a protocol for nondestructive-testing for the first time. HDR imaging protocol has been experimentally validated on images of pitting corrosion in this paper and has been further applied to isolate a range of image backgrounds that arise out of a number of environmental conditions. The superiority of HDR imaging over a standard imaging process has been demonstrated. The results indicate that the proposed protocol is not limited by the examples considered in this paper and can readily be applied to a number of infrastructure maintenance management related applications. The protocol complements and improves standard visual inspection techniques and expert opinions. This approach immediately lends itself to further mathematical and statistical analyses, both qualitative and quantitative.
      436Scopus© Citations 5
  • Publication
    Automated segmentation of nuclei in breast cancer histopathology images
    The process of Nuclei detection in high-grade breast cancer images is quite challenging in the case of image processing techniques due to certain heterogeneous characteristics of cancer nuclei such as enlarged and irregularly shaped nuclei, highly coarse chromatin marginalized to the nuclei periphery and visible nucleoli. Recent reviews state that existing techniques show appreciable segmentation accuracy on breast histopathology images whose nuclei are dispersed and regular in texture and shape; however, typical cancer nuclei are often clustered and have irregular texture and shape properties. This paper proposes a novel segmentation algorithm for detecting individual nuclei from Hematoxylin and Eosin (H&E) stained breast histopathology images. This detection framework estimates a nuclei saliency map using tensor voting followed by boundary extraction of the nuclei on the saliency map using a Loopy Back Propagation (LBP) algorithm on a Markov Random Field (MRF). The method was tested on both whole-slide images and frames of breast cancer histopathology images. Experimental results demonstrate high segmentation performance with efficient precision, recall and dice-coefficient rates, upon testing high-grade breast cancer images containing several thousand nuclei. In addition to the optimal performance on the highly complex images presented in this paper, this method also gave appreciable results in comparison with two recently published methods-Wienert et al. (2012) and Veta et al. (2013), which were tested using their own datasets.
      269Scopus© Citations 45