580 research outputs found

    A review on computer vision based defect detection and condition assessment of concrete and asphalt civil infrastructure

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    To ensure the safety and the serviceability of civil infrastructure it is essential to visually inspect and assess its physical and functional condition. This review paper presents the current state of practice of assessing the visual condition of vertical and horizontal civil infrastructure; in particular of reinforced concrete bridges, precast concrete tunnels, underground concrete pipes, and asphalt pavements. Since the rate of creation and deployment of computer vision methods for civil engineering applications has been exponentially increasing, the main part of the paper presents a comprehensive synthesis of the state of the art in computer vision based defect detection and condition assessment related to concrete and asphalt civil infrastructure. Finally, the current achievements and limitations of existing methods as well as open research challenges are outlined to assist both the civil engineering and the computer science research community in setting an agenda for future research

    Automating Inspection of Tunnels With Photogrammetry and Deep Learning

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    Asset Management of large underground transportation infrastructure requires frequent and detailed inspections to assess its overall structural conditions and to focus available funds where required. At the time of writing, the common approach to perform visual inspections is heavily manual, therefore slow, expensive, and highly subjective. This research evaluates the applicability of an automated pipeline to perform visual inspections of underground infrastructure for asset management purposes. It also analyses the benefits of using lightweight and low-cost hardware versus high-end technology. The aim is to increase the automation in performing such task to overcome the main drawbacks of the traditional regime. It replaces subjectivity, approximation and limited repeatability of the manual inspection with objectivity and consistent accuracy. Moreover, it reduces the overall end-to-end time required for the inspection and the associated costs. This might translate to more frequent inspections per given budget, resulting in increased service life of the infrastructure. Shorter inspections have social benefits as well. In fact, local communities can rely on a safe transportation with minimum levels of disservice. At last, but not least, it drastically improves health and safety conditions for the inspection engineers who need to spend less time in this hazardous environment. The proposed pipeline combines photogrammetric techniques for photo-realistic 3D reconstructions alongside with machine learning-based defect detection algorithms. This approach allows to detect and map visible defects on the tunnel’s lining in local coordinate system and provides the asset manager with a clear overview of the critical areas over all infrastructure. The outcomes of the research show that the accuracy of the proposed pipeline largely outperforms human results, both in three-dimensional mapping and defect detection performance, pushing the benefit-cost ratio strongly in favour of the automated approach. Such outcomes will impact the way construction industry approaches visual inspections and shift towards automated strategies

    Review on Machine Learning-based Defect Detection of Shield Tunnel Lining

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    At present, machine learning methods are widely used in various industries for their high adaptability, optimization function, and self-learning reserve function. Besides, the world-famous cities have almost built and formed subway networks that promote economic development. This paper presents the art states of Defect detection of Shield Tunnel lining based on Machine learning (DSTM). In addition, the processing method of image data from the shield tunnel is being explored to adapt to its complex environment. Comparison and analysis are used to show the performance of the algorithms in terms of the effects of data set establishment, algorithm selection, and detection devices. Based on the analysis results, Convolutional Neural Network methods show high recognition accuracy and better adaptability to the complexity of the environment in the shield tunnel compared to traditional machine learning methods. The Support Vector Machine algorithms show high recognition performance only for small data sets. To improve detection models and increase detection accuracy, measures such as optimizing features, fusing algorithms, creating a high-quality data set, increasing the sample size, and using devices with high detection accuracy can be recommended. Finally, we analyze the challenges in the field of coupling DSTM, meanwhile, the possible development direction of DSTM is prospected

    Automated Defect Detection Tool For Sewer Pipelines

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    In sewer networks, the economic effects and costs that result from a pipeline break are rising sharply. In Qatar, majority of the sewer network pipelines were installed in the last 20 years and are currently in poor condition and constantly deteriorating. As a result, there is huge demand for inspection and rehabilitation of sewer pipelines. In addition to being inaccurate, current Practices of sewer pipelines inspection are time consuming and may not keep up with the deterioration rate of the pipelines. Consequently, this research aims to develop an automated tool to detect different defects such as cracks, deformation, settled deposits and joint displacement in sewer pipelines. The automated approach is dependent upon using image-processing techniques and several mathematical formulas to analyze output data from CCTV camera photos. Given that one inspection session can result in hundreds of CCTV Camera footage, introducing an automated tool would help yield faster results. Additionally, given the subjective nature of most defects, it will result in more systematic results since the current method rely heavily on the operator's experience. The automated tool was able to successfully detect cracks, displaced joints, ovality and settled deposits in pipelines using CCTV Camera inspection output footage. Using two different data sets, the constructed Matlab code could successfully differentiate between cracks and displaced joints with an overall crack detection success rate of 84% and an overall displaced joint detection rate of 94%. The code was also able to efficiently detect settled deposits in the pipelines with a detection rate of 90%. In addition, the automated ovality detection resulted in 100% compatibility with the manual circularity detection

    Detection and Localization of Root Damages in Underground Sewer Systems using Deep Neural Networks and Computer Vision Techniques

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    Indiana University-Purdue University Indianapolis (IUPUI)The maintenance of a healthy sewer infrastructure is a major challenge due to the root damages from nearby plants that grow through pipe cracks or loose joints, which may lead to serious pipe blockages and collapse. Traditional inspections based on video surveillance to identify and localize root damages within such complex sewer networks are inefficient, laborious, and error-prone. Therefore, this study aims to develop a robust and efficient approach to automatically detect root damages and localize their circumferential and longitudinal positions in CCTV inspection videos by applying deep neural networks and computer vision techniques. With twenty inspection videos collected from various resources, keyframes were extracted from each video according to the difference in a LUV color space with certain selections of local maxima. To recognize distance information from video subtitles, OCR models such as Tesseract and CRNN-CTC were implemented and led to a 90% of recognition accuracy. In addition, a pre-trained segmentation model was applied to detect root damages, but it also found many false positive predictions. By applying a well-tuned YoloV3 model on the detection of pipe joints leveraging the Convex Hull Overlap (CHO) feature, we were able to achieve a 20% improvement on the reliability and accuracy of damage identifications. Moreover, an end-to-end deep learning pipeline that involved Triangle Similarity Theorem (TST) was successfully designed to predict the longitudinal position of each identified root damage. The prediction error was less than 1.0 feet

    Defect Detection and Classification in Sewer Pipeline Inspection Videos Using Deep Neural Networks

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    Sewer pipelines as a critical civil infrastructure become a concern for municipalities as they are getting near to the end of their service lives. Meanwhile, new environmental laws and regulations, city expansions, and budget constraints make it harder to maintain these networks. On the other hand, access and inspect sewer pipelines by human-entry based methods are problematic and risky. Current practice for sewer pipeline assessment uses various types of equipment to inspect the condition of pipelines. One of the most used technologies for sewer pipelines inspection is Closed Circuit Television (CCTV). However, application of CCTV method in extensive sewer networks involves certified operators to inspect hours of videos, which is time-consuming, labor-intensive, and error prone. The main objective of this research is to develop a framework for automated defect detection and classification in sewer CCTV inspection videos using computer vision techniques and deep neural networks. This study presents innovative algorithms to deal with the complexity of feature extraction and pattern recognition in sewer inspection videos due to lighting conditions, illumination variations, and unknown patterns of various sewer defects. Therefore, this research includes two main sub-models to first identify and localize anomalies in sewer inspection videos, and in the next phase, detect and classify the defects among the recognized anomalous frames. In the first phase, an innovative approach is proposed for identifying the frames with potential anomalies and localizing them in the pipe segment which is being inspected. The normal and anomalous frames are classified utilizing a one-class support vector machine (OC-SVM). The proposed approach employs 3D Scale Invariant Feature Transform (SIFT) to extract spatio-temporal features and capture scene dynamic statistics in sewer CCTV videos. The OC-SVM is trained by the frame-features which are considered normal, and the outliers to this model are considered abnormal frames. In the next step, the identified anomalous frames are located by recognizing the present text information in them using an end-to-end text recognition approach. The proposed localization approach is performed in two steps, first the text regions are detected using maximally stable extremal regions (MSER) algorithm, then the text characters are recognized using a convolutional neural network (CNN). The performance of the proposed model is tested using videos from real-world sewer inspection reports, where the accuracies of 95% and 86% were achieved for anomaly detection and frame localization, respectively. Identifying the anomalous frames and excluding the normal frames from further analysis could reduce the time and cost of detection. It also ensures the accuracy and quality of assessment by reducing the number of neglected anomalous frames caused by operator error. In the second phase, a defect detection framework is proposed to provide defect detection and classification among the identified anomalous frames. First, a deep Convolutional Neural Network (CNN) which is pre-trained using transfer learning, is used as a feature extractor. In the next step, the remaining convolutional layers of the constructed model are trained by the provided dataset from various types of sewer defects to detect and classify defects in the anomalous frames. The proposed methodology was validated by referencing the ground truth data of a dataset including four defects, and the mAP of 81.3% was achieved. It is expected that the developed model can help sewer inspectors in much faster and more accurate pipeline inspection. The whole framework would decrease the condition assessment time and increase the accuracy of sewer assessment reports

    Surface and Sub-Surface Analyses for Bridge Inspection

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    The development of bridge inspection solutions has been discussed in the recent past. In this dissertation, significant development and improvement on the state-of-the-art in the field of bridge inspection using multiple sensors (e.g. ground penetrating radar (GPR) and visual sensor) has been proposed. In the first part of this research (discussed in chapter 3), the focus is towards developing effective and novel methods for rebar detection and localization for sub-surface bridge inspection of steel rebars. The data has been collected using Ground Penetrating Radar (GPR) sensor on real bridge decks. In this regard, a number of different approaches have been successively developed that continue to improve the state-of-the-art in this particular research area. The second part (discussed in chapter 4) of this research deals with the development of an automated system for steel bridge defect detection system using a Multi-Directional Bicycle Robot. The training data has been acquired from actual bridges in Vietnam and validation is performed on data collected using Bicycle Robot from actual bridge located in Highway-80, Lovelock, Nevada, USA. A number of different proposed methods have been discussed in chapter 4. The final chapter of the dissertation will conclude the findings from the different parts and discuss ways of improving on the existing works in the near future

    Industrial Fluids Components Health Management Using Deep Learning

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    The fatigue state of fluid components such as valves, metal surfaces in gas or oil carrying pipelines is important to monitor on regular basis and plan for repair work to avoid risks associated with them, this becomes more crucial when the pipelines are supplying hazard prone fluids. There exist methods for detection of corroded surfaces, scratches and fractures in pipelines, valves, and regulators etcetera. The conventional methods are based on sensors and chemical analysis methods. There are challenges with conventional methods pertaining to the desired metric of scalability and disadvantages of these methods is they are contact based and destructive methods. Therefore, to overcome these limitations of existing methods there is a need for development of non-contact and nondestructive methods. The recent advancements in Artificial Intelligence technology in every domain including health care monitoring, agriculture sector, defense applications and civilian applications etc., have shown that deep learning methods can be explored in industrial applications to develop fault tolerant systems which help fluid components state of health monitoring through computer vision. In this chapter proposes various methods for analysis of health state of fluid components using deep convolutional neural networks and suggest the best models for these applications

    Deep Learning for Crack-Like Object Detection

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    Cracks are common defects on surfaces of man-made structures such as pavements, bridges, walls of nuclear power plants, ceilings of tunnels, etc. Timely discovering and repairing of the cracks are of great significance and importance for keeping healthy infrastructures and preventing further damages. Traditionally, the cracking inspection was conducted manually which was labor-intensive, time-consuming and costly. For example, statistics from the Central Intelligence Agency show that the world’s road network length has reached 64,285,009 km, of which the United States has 6,586,610 km. It is a huge cost to maintain and upgrade such an immense road network. Thus, fully automatic crack detection has received increasing attention. With the development of artificial intelligence (AI), the deep learning technique has achieved great success and has been viewed as the most promising way for crack detection. Based on deep learning, this research has solved four important issues existing in crack-like object detection. First, the noise problem caused by the textured background is solved by using a deep classification network to remove the non-crack region before conducting crack detection. Second, the computational efficiency is highly improved. Third, the crack localization accuracy is improved. Fourth, the proposed model is very stable and can be used to deal with a wide range of crack detection tasks. In addition, this research performs a preliminary study about the future AI system, which provides a concept that has potential to realize fully automatic crack detection without human’s intervention

    A Perspective on AI-Based Image Analysis and Utilization Technologies in Building Engineering: Recent Developments and New Directions

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    Artificial Intelligence (AI) is a trending topic in many research areas. In recent years, even building, civil, and structural engineering have also started to face with several new techniques and technologies belonging to this field, such as smart algorithms, big data analysis, deep learning practices, etc. This perspective paper collects the last developments on the use of AI in building engineering, highlighting what the authors consider the most stimulating scientific advancements of recent years, with a specific interest in the acquisition and processing of photographic surveys. Specifically, the authors want to focus both on the applications of artificial intelligence in the field of building engineering, as well as on the evolution of recently widespread technological equipment and tools, emphasizing their mutual integration. Therefore, seven macro-categories have been identified where these issues are addressed: photomodeling; thermal imaging; object recognition; inspections assisted by UAVs; FEM and BIM implementation; structural monitoring; and damage identification. For each category, the main new innovations and the leading research perspectives are highlighted. The article closes with a brief discussion of the primary results and a viewpoint for future lines of research
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