221 research outputs found

    Challenges of bridge maintenance inspection

    Get PDF
    Bridges are amongst the largest, most expensive and complex structures, which makes them crucial and valuable transportation asset for modern infrastructure. Bridge inspection is a crucial component of monitoring and maintaining these complex structures. It provides a safety assessment and condition documentation on a regular basis, noting maintenance actions needed to counteract defects like cracks, corrosion and spalling. This paper presents the challenges with existing bridge maintenance inspection as well as an overview on proposed methods to overcome these challenges by automating inspection using computer vision methods. As a conclusion, existing methods for automated bridge inspection are able to detect one class of damage type based on images. A multiclass approach that also considers the 3D geometry, as inspectors do, is missing

    Pavement crack detection and clustering via region-growing algorithm from 3D MLS point clouds

    Get PDF
    Road condition monitoring plays a critical role in transportation infrastructure maintenance and traffic safety assurance. This research introduces a methodology to detect cracks on pavement point clouds acquired with Mobile Laser Scanning systems, which offer more versatility and comprehensive information about the road environment than other specific surveying systems (i.e., profilometers, 3D cameras). The methodology comprises the following steps: (1) Road segmentation; (2) the detection of candidate crack points in individual scanning lines of the point cloud, based on point elevation; (3) crack point clustering via a region-growing algorithm; and (4) crack geometrical attributes extraction. Both the profile evaluation and the region-growing clustering algorithms have been developed from scratch to detect cracks directly from 3D point clouds instead of using raster data or Geo-Referenced Feature images, offering a quick and effective pre-rating tool for pavement condition assessment. Crack detection is validated with data from damaged roads in Portugal.Ministerio de Ciencia e Innovación | Ref. PID2019-105221RB-C43Ministerio de Ciencia e Innovación | Ref. FJC2018-035550-

    Scan4Façade: Automated As-Is Façade Modeling of Historic High-Rise Buildings Using Drones and AI

    Get PDF
    This paper presents an automated as-is façade modeling method for existing and historic high-rise buildings, named Scan4Façade. To begin with, a camera drone with a spiral path is employed to capture building exterior images, and photogrammetry is used to conduct three-dimensional (3D) reconstruction and create mesh models for the scanned building façades. High-resolution façade orthoimages are then generated from mesh models and pixelwise segmented by an artificial intelligence (AI) model named U-net. A combined data augmentation strategy, including random flipping, rotation, resizing, perspective transformation, and color adjustment, is proposed for model training with a limited number of labels. As a result, the U-net achieves an average pixel accuracy of 0.9696 and a mean intersection over union of 0.9063 in testing. Then, the developed twoStagesClustering algorithm, with a two-round shape clustering and a two-round coordinates clustering, is used to precisely extract façade elements’ dimensions and coordinates from façade orthoimages and pixelwise label. In testing with the Michigan Central Station (office tower), a historic high-rise building, the developed algorithm achieves an accuracy of 99.77% in window extraction. In addition, the extracted façade geometric information and element types are transformed into AutoCAD command and script files to create CAD drawings without manual interaction. Experimental results also show that the proposed Scan4Façade method can provide clear and accurate information to assist BIM feature creation in Revit. Future research recommendations are also stated in this paper

    Towards generation of as-damaged BIM models using laser-scanning and as-built BIM: First estimate of as-damaged locations of reinforced concrete frame members in masonry infill structures

    Get PDF
    After an earthquake, Terrestrial Laser Scanning (TLS) can capture point clouds of the damaged state of building facades rapidly, remotely and accurately. A long-term research effort aims to develop applications that can reconstruct ‘as-damaged’ BIM models of reinforced concrete (RC) framed buildings based on their ‘as-built’ BIM models and scans of their ‘as-damaged’ states. This paper focuses on a crucial step: generating an initial ‘best-guess’ for the new locations of the façade structural members. The output serves as the seed for a recursive process in which the location and damage to each object is refined in turn. Locating the ‘as-built’ structural members in the ‘as-damaged’ scan is challenging because each member may have different displacement and damage. An algorithm was developed and tested for the case of reinforced concrete frames with masonry infill walls. It exploits the topology of the frames to map the original structural grid onto the damaged façade. The tests used synthetic datasets prepared from records of two earthquake-damaged buildings. In both cases, the results were sufficiently accurate to allow progress to the following step, assessment of the individual structural members

    Pavement Defect Classification and Localization Using Hybrid Weakly Supervised and Supervised Deep Learning and GIS

    Get PDF
    Automated detection of road defects has historically been challenging for the pavement management industry. As a result, new methods have been developed over the past few years to handle this issue. Most of these methods relied on supervised machine learning techniques, such as object detection and segmentation methods, which need a large, annotated image dataset to train their models. However, annotating pavement defects is difficult and time-consuming due to their ununiformed and complex shapes. To address this challenge, a hybrid pavement defect classification and localization framework using weakly supervised and supervised deep learning methods is proposed in this thesis. This framework has two steps: (1) A robust hierarchical two-level classifier that classifies the defects in images, and (2) A method for defect localization combining weakly supervised and supervised techniques. In the localization method, first, defects are primarily localized using a weakly supervised method (i.e. Class Activation Mapping (CAM)). Next, based on the results of the first classifiers, the defects are segmented from the localized patches obtained in the previous step. The feature maps extracted from the CAM method are used to train a segmentation network once (i.e. U-Net or Mask R-CNN) to localize and segment the defects in the images. Thus, the proposed framework combines the advantages of weakly supervised and supervised methods. The supervised modules in the framework are trained once and can be used for any new data without the need to train. In other words, to use our framework on new dataset only the classifiers should be fine-tuned. Furthermore, the proposed framework introduced an innovative method designed to calculate the maximum crack width in pixels within linear segmented defect patches, derived from the localization module of the proposed framework. This method is particularly advantageous as it provides critical information that can be further employed in the calculation of the Pavement Condition Index (PCI). Additionally, the proposed method benefits from an asset management inspection system based on Geographic Information System (GIS) technology to prepare the dataset used in the training and testing. Thus, this advanced system serves a dual role within our framework. Firstly, it assists in the assembly and preparation of the dataset used in the model training process, providing a geographically organized collection of images and related data. Secondly, it plays a crucial role in the testing phase, offering a spatially accurate platform for evaluating the effectiveness of the model in real-world scenarios. A dataset from Georgia State in the USA was used in the case study. The proposed framework obtained high precision of 97%, 88%, 92% and 97% for localizing the alligator, block, longitudinal and transverse cracks, respectively. Considering all factors, such as annotation cost, and performance on the test dataset, the proposed localization method outperforms the supervised localization methods, such as instance segmentation and object detection for localizing road pavement defect

    Methods for Automated Identification of Roadway Drainage-Related Features from Mobile LiDAR Data

    Get PDF
    Light Detection and Ranging (LiDAR) systems have been increasingly used in project planning, project development, construction, operations, maintenance, and asset management. Typical data collected by a LiDAR system include slant distance, incidence angle, and reflectivity measurements. This research focuses on mobile LiDAR systems (MLSs). Processing of large amounts of data collected by MLSs remains tedious and time-consuming. For MLSs to be used efficiently in roadway drainage inventory and condition assessment, automated methods are needed to identify key features that affect drainage. The aim of this research is to develop computational methods for automated identification of such features from data collected through MLSs. The specific objectives of this research are to a) detect pavement surface type, b) detect the presence of driveways and underlying pipes and extract count, width, elevation difference and material cover and c) detect roadside features such as grass-cover area, curb location, and curb height based on the data collected using a SICK LMS-5XX series LiDAR scanner and hardware and software by Road Doctor. Reflectivity, measured as a logarithmic index of power level called received signal strength indicator (RSSI), is used to develop an algorithm to detect surface type based on statistical analysis of RSSI. Cross-sectional geometry, along with material identification, is used to identify driveways and underlying pipes. RSSI distribution and material identification techniques are used to detect roadside grass areas. Elevation distribution and filter template technique are used to detect curbs. Each method was tested and validated using data from actual road sections in Texas. The ability to detect aforementioned features reliably using automated means is an initial step to further the cause of MLS acceptance and implementation. Generally, the accuracies of pavement and grass detection methods were at least 83%. The effect of reflectivity attenuation is pronounced for roadside. Therefore, in order to develop a reliable grass detection method, attenuation correction is required. It is possible to detect driveways and distinguish them from topographical features using a combination of elevation cross sections, material detection, and surface smoothness. It is possible to identify curbs using filter template technique

    Automated Extraction of Road Information from Mobile Laser Scanning Data

    Get PDF
    Effective planning and management of transportation infrastructure requires adequate geospatial data. Existing geospatial data acquisition techniques based on conventional route surveys are very time consuming, labor intensive, and costly. Mobile laser scanning (MLS) technology enables a rapid collection of enormous volumes of highly dense, irregularly distributed, accurate geo-referenced point cloud data in the format of three-dimensional (3D) point clouds. Today, more and more commercial MLS systems are available for transportation applications. However, many transportation engineers have neither interest in the 3D point cloud data nor know how to transform such data into their computer-aided model (CAD) formatted geometric road information. Therefore, automated methods and software tools for rapid and accurate extraction of 2D/3D road information from the MLS data are urgently needed. This doctoral dissertation deals with the development and implementation aspects of a novel strategy for the automated extraction of road information from the MLS data. The main features of this strategy include: (1) the extraction of road surfaces from large volumes of MLS point clouds, (2) the generation of 2D geo-referenced feature (GRF) images from the road-surface data, (3) the exploration of point density and intensity of MLS data for road-marking extraction, and (4) the extension of tensor voting (TV) for curvilinear pavement crack extraction. In accordance with this strategy, a RoadModeler prototype with three computerized algorithms was developed. They are: (1) road-surface extraction, (2) road-marking extraction, and (3) pavement-crack extraction. Four main contributions of this development can be summarized as follows. Firstly, a curb-based approach to road surface extraction with assistance of the vehicle’s trajectory is proposed and implemented. The vehicle’s trajectory and the function of curbs that separate road surfaces from sidewalks are used to efficiently separate road-surface points from large volume of MLS data. The accuracy of extracted road surfaces is validated with manually selected reference points. Secondly, the extracted road enables accurate detection of road markings and cracks for transportation-related applications in road traffic safety. To further improve computational efficiency, the extracted 3D road data are converted into 2D image data, termed as a GRF image. The GRF image of the extracted road enables an automated road-marking extraction algorithm and an automated crack detection algorithm, respectively. Thirdly, the automated road-marking extraction algorithm applies a point-density-dependent, multi-thresholding segmentation to the GRF image to overcome unevenly distributed intensity caused by the scanning range, the incidence angle, and the surface characteristics of an illuminated object. The morphological operation is then implemented to deal with the presence of noise and incompleteness of the extracted road markings. Fourthly, the automated crack extraction algorithm applies an iterative tensor voting (ITV) algorithm to the GRF image for crack enhancement. The tensor voting, a perceptual organization method that is capable of extracting curvilinear structures from the noisy and corrupted background, is explored and extended into the field of crack detection. The successful development of three algorithms suggests that the RoadModeler strategy offers a solution to the automated extraction of road information from the MLS data. Recommendations are given for future research and development to be conducted to ensure that this progress goes beyond the prototype stage and towards everyday use

    Quantitative Assessment of Scots Pine (Pinus Sylvestris L.) Whorl Structure in a Forest Environment Using Terrestrial Laser Scanning

    Get PDF
    State-of-the-art technology available at sawmills enables measurements of whorl numbers and the maximum branch diameter for individual logs, but such information is currently unavailable at the wood procurement planning phase. The first step toward more detailed evaluation of standing timber is to introduce a method that produces similar wood quality indicators in standing forests as those currently used in sawmills. Our aim was to develop a quantitative method to detect and model branches from terrestrial laser scanning (TLS) point clouds data of trees in a forest environment. The test data were obtained from 158 Scots pines (Pinus sylvestris L.) in six mature forest stands. The method was evaluated for the accuracy of the following branch parameters: Number of whorls per tree and for every whorl, the maximum branch diameter and the branch insertion angle associated with it. The analysis concentrated on log-sections (stem diameter > 15 cm) where the branches most affect wood's value added. The quantitative whorl detection method had an accuracy of 69.9% and a 1.9% false positive rate. The estimates of the maximum branch diameters and the corresponding insertion angles for each whorl were underestimated by 0.34 cm (11.1%) and 0.67 degrees (1.0%), with a root-mean-squared error of 1.42 cm (46.0%) and 17.2 degrees (26.3%), respectively. Distance from the scanner, occlusion, and wind were the main external factors that affect the method's functionality. Thus, the completeness and point density of the data should be addressed when applying TLS point cloud based tree models to assess branch parameters.Peer reviewe
    • …
    corecore