Damage detection with an autonomous UAV using deep learning

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Civil infrastructure is important to ensure the ongoing functionality of human living environments. However, in North America, much of the infrastructure is aging and requires continuous monitoring and maintenance to ensure the safety of people. Traditionally, visual inspection has been carried out to monitor the health of such structures. However, assessments require trained inspectors, and monitoring methods are difficult due to the size and location of the infrastructure. Recently, data acquisition using unmanned aerial vehicles (UAVs) equipped with cameras has been growing in popularity, and research has been conducted concerning the use of UAVs for the visual inspection of infrastructure. However, UAV inspection requires skilled pilots and the use of a global positioning system (GPS) for autonomous flight. Unfortunately, for some locations, a GPS signal cannot be reached for autonomous flight of the UAV. For example, the GPS signal on the inside of a building or underneath a bridge deck is unreliable, but these locations also require inspections to ensure structural health. In order to address this issue, autonomous UAV methods using ultrasonic beacons have been proposed. Beacons are able to provide positional data allowing UAVs to perform the autonomous mission. As an example of structural damage, we report the successful detection of concrete cracks using a deep convolutional neural network by processing the video data collected from an autonomous UAV.