Now showing 1 - 10 of 20
  • 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.
      382Scopus© Citations 46
  • 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.
      560Scopus© Citations 19
  • Publication
    A comparison of image based 3D recovery methods for underwater inspections
    Offshore structures can be subjected to millions of variable amplitude load cycles during their service life which is the primary cause of structural deterioration. Such fatigue loading is exacerbated by marine growth colonization which changes the surface roughness characteristics and increases the diameter of structural members. Having an accurate knowledge of these parameters is essential for analyzing the increased hydrodynamic forces acting on the structure. This paper addresses the issue of acquiring shape information by comparing two popular classes of image based shape recovery techniques; stereo photography and Structure from Motion (SfM). Stereo photography utilises a dual camera set-up to simultaneously photograph an object of interest from slightly different viewpoints, whilst SfM methods generally involve a single camera moving in a static scene. In this paper, these techniques are performed on a controlled shape in an underwater setting, as well as synthetic data which allows for an irregular shape typical of marine growth to be tested whilst still having knowledge of the exact geometrical shape. The results reveal that the self-calibrated stereo approach fared well at getting an appropriately scaled full metric reconstruction, whilst the SfM approach was more susceptible to breaking down.
  • 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.
      215Scopus© Citations 36
  • Publication
    Texture Analysis Based Damage Detection of Ageing Infrastructural Elements
    To make visual data a part of quantitative assessment for infrastructure maintenance management, it is important to develop computer-aided methods that demonstrate efficient performance in the presence of variability in damage forms, lighting conditions, viewing angles, and image resolutions taking into account the luminous and chromatic complexities of visual data. This article presents a semi-automatic, enhanced texture segmentation approach to detect and classify surface damage on infrastructure elements and successfully applies them to a range of images of surface damage. The approach involves statistical analysis of spatially neighboring pixels in various color spaces by defining a feature vector that includes measures related to pixel intensity values over a specified color range and statistics derived from the Grey Level Co-occurrence Matrix calculated on a quantized grey-level scale. Parameter optimized non-linear Support Vector Machines are used to classify the feature vector. A Custom-Weighted Iterative model and a 4-Dimensional Input Space model are introduced. Receiver Operating Characteristics are employed to assess and enhance the detection efficiency under various damage conditions.
      359Scopus© Citations 88
  • 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
    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
    Effects of Turbidity and Lighting on the Performance of an Image Processing based Damage Detection Technique
    Measuring the true extent of damage in a structure remains a difficult task for inspectors. For visual inspections, an accurate assessment of the damage state is often subjective in nature and prone to error, especially when an inspection is conducted in hostile surroundings or when there are challenging environmental conditions present. While incorporating some form of Non-Destructive Technique (NDT) is generally useful for the inspection process, its performance may similarly degrade in the presence of environmental conditions. It is thus of great practical importance to have a measure of the performance of an NDT for a host of varying conditions, thereby allowing the inspector to determine whether it could be successfully applied in a given situation. In this paper, a measure of the effectiveness of an NDT is probabilistically determined for various environmental conditions through the use of Receiver Operating Characteristic (ROC) curves. ROC curves offer a convenient way of characterizing and comparing the performance of an NDT under various conditions. The NDT considered in this paper is an image processing based damage detection technique which uses texture information in conjunction with Support Vector Machine (SVM) classification to identify damaged regions. The variability of this technique is evaluated for various damage forms that are subjected to two changing parameters; turbidity and lighting. There were three set levels (low, medium, high) for each parameter. The conditions that were conducive to good detection were isolated and ranked using the α-δ method as part of the ROC analysis. The technique is applied to standard dynamic range (SDR) images and high dynamic range (HDR) images in order to assess their respective sensitivities to the changing parameters.
  • Publication
    Evaluation of Camera Calibration Techniques for Quantifying Deterioration
    (Civil Engineering Research Association of Ireland, 2016-08-30) ; ; ;
    Imaging systems offer an efficient way of obtaining quantitative information on the health status of structural components. They hold particular value for underwater inspections as they can be easily adapted for underwater use and they enable physical information to be captured from a scene for the purpose of later analysis. In order to make the visual data a part of a quantitative assessment, it is necessary to calibrate the imaging systems so that photographed instances of damage can be expressed and measured in physically meaningful real world units, such as millimetres, which can then be used by engineers in subsequent analyses. The imaging system employed in this study is a stereo rig. It consists of two synchronised cameras that capture images of the scene from slightly different perspectives, thereby encoding depth information. This paper evaluates and compares two main approaches for calibrating such a stereo systems, namely, the classical checkerboard procedure and self-calibration based on Kruppa’s equations. Conventional checkerboard calibration must be carried out on-site by photographing a planar checkerboard pattern that is held at multiple random poses, while self-calibration can be carried out after-the-fact and relies only on the static scene acting as a constraint on the camera parameters. The performance of each approach is assessed through a set of experiments performed on controlled real-world specimens as well as on synthetic data. Results indicate that checkerboard calibration is slightly more accurate than self-calibration; however, the practical advantages of using self-calibration may outweigh this reduction in accuracy. An understanding of the advantages and limitations associated with each camera calibration allows inspectors to rationalise the use of either approach as part of their inspection regime, and it helps them to fully capitalise on the benefits of image-based methods.
  • 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.