![]() The approach could also be expanded to other sensors such as multispectral or hyperspectral sensors, real-time stereo, LiDAR, chemical sniffers or any other sensor capable of detecting the desired anomalies. Many possible approaches to detection are possible, including cascade classifiers, neural networks or change detection between monitoring flights and a known baseline dataset. Because of these constraints, creating a single detailed 3D model of a large infrastructure object is in many cases impractical.Īlthough there is a large body of work relating to UAVs tracking moving objects and real-time detection of specific objects, real-time detection of unknown anomalies is a challenging problem, and this paper does not attempt to address it directly. While excellent results can be obtained for single site projects as demonstrated by, UAV flight time, computational power, data storage and model processing time all constrain the scalability of this technology to large-scale infrastructure systems, such as pipelines, canals, levees, railroads, utility lines and other long linear features. However, current UAV and 3D reconstruction technology still has limitations. In the field of infrastructure monitoring, the clear advantages of on-demand, high precision 3D modeling are driving companies and researchers to explore the possibilities of this technology (for an excellent overview, see ). Together with the increasing ease of obtaining imagery, advances in computer vision and computer processing power have led to a widespread increase in aerial mapping and 3D-reconstruction. The advent of small Unmanned Aerial Systems (sUAS) has given rise to a host of new applications for aerial imaging technology in many fields. When compared to a low speed, low elevation traditional flight, the proposed method is shown in simulation to decrease total flight time by up to 55%, while reducing the amount of image data to be processed by 89% and maintaining 3D model accuracy at areas of interest. The new flights are compared to traditional flights in terms of flight time, data collected and 3D model accuracy. The algorithm is used to plan inspection flights for the anomaly locations, and simulated images from the flights are rendered and processed to construct 3D models of the locations of interest. A simulated canal environment is constructed, and several simulated anomalies are created and marked. The study begins by assuming that anomaly detections are possible and focuses on quantifying the potential benefits of the combined method and the view planning algorithm. ![]() This paper presents a novel method for UAV-based 3D modeling of large infrastructure objects, such as pipelines, canals and levees, that combines anomaly detection with automatic on-board 3D view planning. ![]()
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