Automated health monitoring and maintenance of civil infrastructure systems is an active yet challenging area of research. Current structure inspection standards require an inspector to visually assess structure conditions. A less time-consuming and inexpensive alternative to current monitoring methods is to use a robotic system that can inspect structures more frequently, and perform autonomous damage detection. Nondestructive evaluation techniques (NDE) are innovative approaches for structural health monitoring. Among several possible techniques, the use of optical instrumentation (e.g., digital cameras), image processing and computer vision are promising approaches as nondestructive testing methods for structural health monitoring to complement sensor-based approaches. The feasibility of using image processing techniques to detect deterioration in structures has been acknowledged by leading researches in the field. This study represents the efforts that have been taken place by the author to form, implement, and evaluate several vision-based approaches that are promising for robust condition assessment of structures. ❧ It is well-recognized that civil infrastructure monitoring approaches that rely on visual approaches will continue to be an important methodology for condition assessment of such systems. Part of this study presents and evaluates the underlying technical elements for the development of an integrated inspection software tool that is based on the use of inexpensive digital cameras. For this purpose, digital cameras are appropriately mounted on a structure (e.g., a bridge) and can zoom or rotate in three directions (similar to traffic cameras). They are remotely controlled by an inspector, which allows the visual assessment of the structure’s condition by looking at images captured by the cameras. By not having to travel to the structures site, other issues related to safety considerations and traffic detouring are consequently bypassed. The proposed system gives an inspector the ability to compare the current (visual) situation of a structure with its former condition. If an inspector notices a defect in the current view, he/she can request a reconstruction of the same view using images that were previously captured and automatically stored in a database. Furthermore, by generating databases that consist of periodically captured images of a structure, the proposed system allows an inspector to evaluate the evolution of changes by simultaneously comparing the structure’s condition at different time periods. The essential components of the proposed virtual image reconstruction system are: keypoint detection, keypoint matching, image selection, outlier exclusion, bundle adjustment, composition, and cropping. Several illustrative examples are presented to demonstrate the capabilities, as well as the limitations, of the proposed vision-based inspection procedure. ❧ Visual inspection of structures is a highly qualitative method. If a region is inaccessible, binoculars must be used to detect and characterize defects. Although several NDE methods have been proposed for inspection purposes, they are nonadaptive and cannot quantify crack thickness reliably. A contact-less remote-sensing crack detection and quantification methodology based on 3D scene reconstruction (computer vision), image processing, and pattern recognition concepts is introduced. The proposed approach utilizes depth perception to detect cracks and quantify their thickness, thereby giving a robotic inspection system the ability to analyze images captured from any distance and using any focal length or resolution. This unique adaptive feature is especially useful for incorporating mobile systems, such as unmanned aerial vehicles (UAV), into structural inspection methods since it would allow inaccessible regions to be properly inspected for cracks. Guidelines are presented for optimizing the acquisition and processing of images, thereby enhancing the quality and reliability of the damage detection approach and allowing the capture of even the slightest cracks (e.g., detection of 0.1 mm cracks from a distance of 20 m), which are routinely encountered in realistic field applications where the camera-object distance and image contrast are not controllable. ❧ Corrosion is another crucial defect in structural systems that can lead to catastrophe if it is neglected. A novel adaptive approach based on multi-resolution wavelet analysis, color analysis and depth perception is proposed that drastically improves the performance of the defect detection algorithm. The main contribution of this part is the integration of the depth perception with the pattern classification algorithms, which has never been done in previous studies. Several analytical evaluations are presented to illustrate the capabilities of the proposed system. Furthermore, the area of the corroded regions are quantified using the retrieved depth information. ❧ Insufficient inspections and maintenance of sewer pipes are the primary causes of today’s poor pipeline conditions. CCTV surveys, which is the most commonly used pipeline inspection technique in the United States, are both costly and laborious. Furthermore, they are subjective to an inspector’s level of experience, attentiveness, and fatigue. A preliminary study on autonomous condition assessment of sewer pipelines based on CCTV surveys is presented. The proposed system will analyze CCTV video frames and provide inspectors with a report about probable defects and their precise locations. With this system, an inspector is not required to watch an entire CCTV video; rather, he only has to assess the locations suggested by the system. Several segmentation techniques are tested and evaluated to extract joints, laterals, and cracks in sewer pipelines. Morphological operators are most effective in this regard. Several examples are presented to illustrate the capabilities of these promising algorithms. ❧
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