Pavement management systems require detailed information of the current state of the roads to take appropriate actions to optimize expenditure on maintenance and rehabilitation. In particular, the presence of cracks is a cardinal aspect to be considered. This article presents a solution based on an instrumented vehicle equipped with an imaging system, two Inertial Profilers, a Differential Global Positioning System, and a webcam. Information about the state of the road is acquired at normal road speed. A method based on the use of Gabor filters is used to detect the longitudinal and transverse cracks. The methodologies used to create Gabor filter banks and the use of the filtered images as descriptors for subsequent classifiers are discussed in detail. Three different methodologies for setting the threshold of the classifiers are also evaluated. Finally, an AdaBoost algorithm is used for selecting and combining the classifiers, thus improving the results provided by a single classifier. A large database has been acquired and used to train and test the proposed system and methods, and suitable results have been obtained in comparison with other reference works. C 2013 Computer-Aided Civil and Infrastructure Engineering.
Abstract:The classification of the images taken during the measurement of an architectural asset is an essential task within the digital documentation of cultural heritage. A large number of images are usually handled, so their classification is a tedious task (and therefore prone to errors) and habitually consumes a lot of time. The availability of automatic techniques to facilitate these sorting tasks would improve an important part of the digital documentation process. In addition, a correct classification of the available images allows better management and more efficient searches through specific terms, thus helping in the tasks of studying and interpreting the heritage asset in question. The main objective of this article is the application of techniques based on deep learning for the classification of images of architectural heritage, specifically through the use of convolutional neural networks. For this, the utility of training these networks from scratch or only fine tuning pre-trained networks is evaluated. All this has been applied to classifying elements of interest in images of buildings with architectural heritage value. As no datasets of this type, suitable for network training, have been located, a new dataset has been created and made available to the public. Promising results have been obtained in terms of accuracy and it is considered that the application of these techniques can contribute significantly to the digital documentation of architectural heritage.
In this article, a system for the detection of cracks in concrete tunnel surfaces, based on image sensors, is presented. Both data acquisition and processing are covered. Linear cameras and proper lighting are used for data acquisition. The required resolution of the camera sensors and the number of cameras is discussed in terms of the crack size and the tunnel type. Data processing is done by applying a new method called Gabor filter invariant to rotation, allowing the detection of cracks in any direction. The parameter values of this filter are set by using a modified genetic algorithm based on the Differential Evolution optimization method. The detection of the pixels belonging to cracks is obtained to a balanced accuracy of 95.27%, thus improving the results of previous approaches.
In this paper, a comprehensive automatic visual inspection system for detecting pavement cracks, built around a Laser Road Inspection System (LRIS) onboard an instrumented vehicle, is presented. Two inertial profilers, a Differential Global Position System (DGPS), a high-definition camera and a high-speed area scan camera are the additional acquisition equipment. Visual appearance and geometrical information are obtained simultaneously since 3D profiles are obtained by capturing the laser line projected by the LRIS with the external area scan camera. Using AdaBoost algorithm for the combination of the processing results of these two types of data allows us to improve surface crack detection rates.
Obtaining virtual models from real buildings, terrains, or building works is a matter of increased interest in construction. The application of such models ranges from technical use in architecture and civil engineering, to multimedia presentation, or remote visits through the web. This is becoming possible thanks to recent advances in laser scanning technology and related 3D processing algorithms. Moreover, real texture mapped onto 3D models is often required for communication, cataloguing, or digital documentation projects. In this article, an effective methodology to obtain digital building documentation based on 3D textured models is presented. First of all, a brief presentation of laser scanners is given as their data are used. An approach for mapping photographic images onto 3D models is also presented. The proposed approach, based on a camera registration method, offers high flexibility as it is based on hand-held cameras and can be implemented in a computing-effective way. A method for automatic image selection in overlapped areas is also presented. Finally, some hints are given concerning the automatic extraction of sections, orthophotos, and feature lines from the models. Experimental results focused on heritage buildings are shown, which demonstrate the suitability of the proposed techniques.
Pavement maintenance requires knowing the state of the road surface. Human inspection is the most common method for evaluating this state. Recently, the automated visual inspection has been addressed, but some important questions remain open concerning the variable ambient lighting, shadows, device synchronisation and the large amount of data. In the present paper, an automated visual inspection system is presented. Images are obtained using laser lighting and linear cameras onboard a vehicle. Longitudinal and transversal cracks are detected and classified using a novel approach based on combining traditional features and Gabor filters. A Differential Global Positioning System (DGPS), a web camera and an Inertial Profiler to measure the International Roughness Index (IRI) are also considered in order to obtain comprehensive information about the road state. Implementation details are given concerning image acquisition and processing, system architecture and data synchronisation. Field results are presented which prove the suitability of the approach.
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