Complementary advances in computer vision and new sensing platforms have mobilized the research community to pursue automated methods for vision-based visual evaluation of our civil infrastructure. Spatial and temporal limitations typically associated with sensing in large-scale structures are being torn down through the use of low-cost aerial platforms with integrated high-resolution visual sensors. Despite the enormous efforts expended to implement such technology, practical real-world challenges still hinder the application of these methods. The large volumes of complex visual data, collected under uncontrolled circumstances (e.g. varied lighting, cluttered regions, occlusions, and variations in environmental conditions), impose a major challenge to such methods, especially when only a tiny fraction of them are used for conducting the actual assessment. Such difficulties induce undesirable high rates of false-positive and false-negative errors, reducing both trustworthiness and efficiency in the methods. To overcome these inherent challenges, a novel automated image localization and classification technique is developed to extract the regions of interest on each of the images, which contain the targeted region for inspection. Regions of interest are extracted here using structure-from-motion algorithm. Less useful regions of interest, such as those corrupted by occlusions, are then filtered effectively using a robust image classification technique, based on convolutional neural networks. Then, such highly relevant regions of interest are available for visual assessment. The capability of the technique is successfully demonstrated using a full-scale highway sign truss with welded connections.

Perovskite-type Nd 1-x Sr x CoO 3-y catalysts with various Sr mole fraction were prepared and investigated for the effect of Sr substitution on their catalytic activities in the oxidation of carbon monoxide. Utilizing the static and flow methods, kinetic studies have been carried out between 373 and 523 K. The initial reaction was investigated by the static reactor system using a differential photoacoustic cell, and for the study of reaction stage showing a constant catalytic activity after an initial stage characterized by high reaction rates, the flow reactor system using on-line gas chromatography was employed. The catalytic activity increased with increasing amounts of Sr substitution for Nd in NdCoO 3 compounds, and it also increased with higher reaction temperature within the range of 373-523 K. Kinetic data obtained in an initial reaction stage by CO 2 photoacoustic spectroscopy showed that the reaction partial orders to CO and O 2 were 0.8-0.9 and 0, respectively. In the reaction stage showing a constant catalytic activity after an initial stage, the oxidation was found to be first order with respect to CO and 0.5 order with respect to O 2 . The concentration of oxygen vacancy in the solid catalyst was shown to be the controlling factor for the oxidation of carbon monoxide. According to the experimental results, the mechanisms of the CO oxidation processes are discussed, and it is believed that O 2 adsorbs on the oxygen vacancies (V o x ) formed by Sr substitution while CO adsorbs on the lattice oxygens during the reaction process.

After a disaster strikes an urban area, damage to the façades of a building may produce dangerous falling hazards that jeopardize pedestrians and vehicles. Thus, building façades must be rapidly inspected to prevent potential loss of life and property damage. Harnessing the capacity to use new vision sensors and associated sensing platforms, such as unmanned aerial vehicles (UAVs) would expedite this process and alleviate spatial and temporal limitations typically associated with human-based inspection in high-rise buildings. In this paper, we have developed an approach to perform rapid and accurate visual inspection of building façades using images collected from UAVs. An orthophoto corresponding to any reasonably flat region on the building (e.g., a façade or building side) is automatically constructed using a structure-from-motion (SfM) technique, followed by image stitching and blending. Based on the geometric relationship between the collected images and the constructed orthophoto, high-resolution region-of-interest are automatically extracted from the collected images, enabling efficient visual inspection. We successfully demonstrate the capabilities of the technique using an abandoned building of which a façade has damaged building components (e.g., window panes or external drainage pipes).

Low-cost, high-performance vision sensors in conjunction with aerial sensing platforms are providing new possibilities for achieving autonomous visual inspection in civil engineering structures. A large volume of images of a given structure can readily be collected for use in visual inspection, overcoming spatial and temporal limitations associated with human-based inspection. Although researchers have explored several algorithms and techniques for vision-based inspection in recent decades, a major challenge in past implementations lies in dealing with a high volume of images while only a small fraction of them are important for actual inspection. Because processing irrelevant images can generate a significant number of false-positives, automated visual inspection techniques should be used in coordination with methods to localize relevant regions on the images. When combined, automated visual inspection will be able to meet the objectives and quality of human visual inspection. To enable this technology, we develop and validate a novel automated image localization technique to extract regions of interest (ROIs) on each of the images before utilizing vision-based damage detection techniques. ROIs are the portions of an image that contain the physical region of the structure that is targeted for visual interrogation, denoted as the targeted region of interest (TRI). ROIs are computed based on the geometric relationship between the collected images and the TRIs. Analysis of such highly relevant and localized images would enable efficient and reliable visual inspection. We successfully demonstrate the capability of the technique to extract the ROIs using a full-scale highway sign structure in the case where weld connections serve as the TRIs.

For the last two decades, two related approaches have been studied independently in conjunction with limitations of image sensors. The one is to reconstruct a high-resolution (HR) image from multiple low-resolution (LR) observations suffering from various degradations such as blur, geometric deformation, aliasing, noise, spatial sampling and so on. The other one is to reconstruct a high dynamic range (HDR) image from differently exposed multiple low dynamic range (LDR) images. LDR is due to the limitation of the capacitance of analogue-to-digital converter and the nonlinearity of the imaging system's response function. In practical situations, since observations suffer from limitations of both spatial resolution and dynamic range, it is reasonable to address them in a unified context. Most super-resolution (SR) image reconstruction methods that enhance the spatial resolution assume that the dynamic ranges of observations are the same or the imaging system's response function is already known. In this paper, the conventional approaches are overviewed and the SR image reconstruction, which simultaneously enhances spatial resolution and dynamic range, is proposed. The image degradation process including limited spatial resolution and limited dynamic range is modelled. With the observation model, the maximum a posteriori estimates of the response function of the imaging system as well as the single HR image and HDR image are obtained. Experimental results indicate that the proposed algorithm outperforms the conventional approaches that perform the HR and HDR reconstructions sequentially with respect to both objective and subjective criteria.