191 research outputs found

    Crack detection using enhanced thresholding on UAV based collected images

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    © 2018 Australasian Robotics and Automation Association. All rights reserved. This paper proposes a thresholding approach for crack detection in an unmanned aerial vehicle (UAV) based infrastructure inspection system. The proposed algorithm performs recursively on the intensity histogram of UAV-taken images to exploit their crack-pixels appearing at the low intensity interval. A quantified criterion of interclass contrast is proposed and employed as an object cost and stop condition for the recursive process. Experiments on different datasets show that our algorithm outperforms different segmentation approaches to accurately extract crack features of some commercial buildings

    Automatic Surface Crack Detection in Concrete Structures Using OTSU Thresholding and Morphological Operations

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    Concrete cracking is a ubiquitous phenomenon, present in all types of concrete structures. Identifying and tracking the amount and severity of cracking is paramount to evaluating the current condition and predicting the future service life of a concrete asset. Concrete cracks can indicate reinforcement corrosion, the development of spalls or changing support conditions. Therefore, monitoring cracks during the life span of concrete structures has been an effective technique to evaluate the level of safety and preparing plans for future appropriate rehabilitation. One growing technique are unmanned inspections using Unmanned Aerial Vehicles (UAV). UAVs are drones equipped with cameras, sensors, GPS, etc. RGB images (color images in Red, Green and Blue color space) are obtained from a camera mounted on a UAV flying around the structure, to detect cracks and other defects. Each image captured by UAV needs to be evaluated to track the crack formations. To save time, this task can be done by applying image processing techniques to automatically detect and report cracks rather than using a human to identify them. In addition, processing RGB images with sufficient information, such as the distance of camera to surface for each picture, will provide the dimension of the cracks (length and width). The report consists of the following sections: A literature review of image processing techniques used in structural health monitoring and other fields of interest is provided in chapter 2. The Proposed method to identify cracks is demonstrated in Chapter 3. Experimental results, conclusion and future work are presented in Chapter 4. Appendix A includes the processed images using the proposed method and Appendix B includes the comparison between Talab’s method and the proposed method. In Appendix C, a “readme” file is given to run the program, and finally Appendix D shows the Matlab Code

    AI-Enabled Contextual Representations for Image-based Integration in Health and Safety

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    Recent advancements in the area of Artificial Intelligence (AI) have made it the field of choice for automatically processing and summarizing information in big-data domains such as high-resolution images. This approach, however, is not a one-size-fits-all solution, and must be tailored to each application. Furthermore, each application comes with its own unique set of challenges including technical variations, validation of AI solutions, and contextual information. These challenges are addressed in three human-health and safety related applications: (i) an early warning system of slope failures in open-pit mining operations; (ii) the modeling and characterization of 3D cell culture models imaged with confocal microscopy; and (iii) precision medicine of biomarker discovery from patients with glioblastoma multiforme through digital pathology. The methodologies and results in each of these domains show how tailor-made AI solutions can be used for automatically extracting and summarizing pertinent information from big-data applications for enhanced decision making

    Deep Learning Approaches in Pavement Distress Identification: A Review

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    This paper presents a comprehensive review of recent advancements in image processing and deep learning techniques for pavement distress detection and classification, a critical aspect in modern pavement management systems. The conventional manual inspection process conducted by human experts is gradually being superseded by automated solutions, leveraging machine learning and deep learning algorithms to enhance efficiency and accuracy. The ability of these algorithms to discern patterns and make predictions based on extensive datasets has revolutionized the domain of pavement distress identification. The paper investigates the integration of unmanned aerial vehicles (UAVs) for data collection, offering unique advantages such as aerial perspectives and efficient coverage of large areas. By capturing high-resolution images, UAVs provide valuable data that can be processed using deep learning algorithms to detect and classify various pavement distresses effectively. While the primary focus is on 2D image processing, the paper also acknowledges the challenges associated with 3D images, such as sensor limitations and computational requirements. Understanding these challenges is crucial for further advancements in the field. The findings of this review significantly contribute to the evolution of pavement distress detection, fostering the development of efficient pavement management systems. As automated approaches continue to mature, the implementation of deep learning techniques holds great promise in ensuring safer and more durable road infrastructure for the benefit of society

    A Systematic Review of Convolutional Neural Network-Based Structural Condition Assessment Techniques

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    With recent advances in non-contact sensing technology such as cameras, unmanned aerial and ground vehicles, the structural health monitoring (SHM) community has witnessed a prominent growth in deep learning-based condition assessment techniques of structural systems. These deep learning methods rely primarily on convolutional neural networks (CNNs). The CNN networks are trained using a large number of datasets for various types of damage and anomaly detection and post-disaster reconnaissance. The trained networks are then utilized to analyze newer data to detect the type and severity of the damage, enhancing the capabilities of non-contact sensors in developing autonomous SHM systems. In recent years, a broad range of CNN architectures has been developed by researchers to accommodate the extent of lighting and weather conditions, the quality of images, the amount of background and foreground noise, and multiclass damage in the structures. This paper presents a detailed literature review of existing CNN-based techniques in the context of infrastructure monitoring and maintenance. The review is categorized into multiple classes depending on the specific application and development of CNNs applied to data obtained from a wide range of structures. The challenges and limitations of the existing literature are discussed in detail at the end, followed by a brief conclusion on potential future research directions of CNN in structural condition assessment

    Detecting and Evaluating Cracks on Aging Concrete Members with Deep Convolutional Neural Networks

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    Cracks in concrete structures are evaluated through a timely and subjective manual inspection. The location of cracks is often recorded in an inspection report where some cracks are measured. Although measurements or locations may not be necessary for all cracks observed in concrete members, if quantitative data can be gathered in an autonomous way, allowing measurement data to be used in tracking changes in spatial and temporal scales, this quantitative data can provide useful information not yet captured in the manual inspection process. This thesis aims to construct an image-based crack detection and evaluation pipeline that can assist health monitoring of aging concrete structures, by providing crack locations and measured crack properties for the entire structural member. Over 16,000 images of aging concrete bridge deck were collected from cameras attached on an unmanned aerial vehicle, machine vision cameras attached on a ground vehicle, and other literature. Mask and Region based Convolutional Neural Network (Mask R-CNN) was utilized to train 256 by 256-pixel patches of collected images using three distinct training strategies to detect and segment concrete cracks on bridge decks. Resulting crack masks were translated into binary data (crack or non-crack pixels) and skeletons of the mask were created where the Euclidean distance from the center of the skeleton to the edge of the mask were measured. This allowed to calculate the relative crack width, length, and orientation of each detected crack. Relative crack properties were transformed into real-world unites using the ground sampling distance of the host image. Image patches were then compiled to construct a crack map of the entire structural member. A case study was conducted on the deck and pier of an aging concrete bridge to test the robustness of the proposed data pipeline. The study yielded that the model was able to successfully detect cracks with an average width of 0.020 inches and were able to make accurate measurements of crack widths that are larger than 0.080 inches. In order to improve the measurements for smaller crack widths, the ground sampling distance needs to be to the scale of the crack width in interest. The image-based data pipeline developed in this study demonstrates potential for the application in autonomous inspections of concrete members. In addition, the data pipeline can be used as a reference framework to provide an example on how computer-vision based data analytics can provide useful information for structural inspections of aging concrete members. Advisor: Chungwook Si

    Non-Contact Evaluation Methods for Infrastructure Condition Assessment

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    The United States infrastructure, e.g. roads and bridges, are in a critical condition. Inspection, monitoring, and maintenance of these infrastructure in the traditional manner can be expensive, dangerous, time-consuming, and tied to human judgment (the inspector). Non-contact methods can help overcoming these challenges. In this dissertation two aspects of non-contact methods are explored: inspections using unmanned aerial systems (UASs), and conditions assessment using image processing and machine learning techniques. This presents a set of investigations to determine a guideline for remote autonomous bridge inspections
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