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Drone-Based Corrosion Detection on High-Voltage Transmission Towers Based on Spectral Angle Classification and Clustering

Abstract:

To evaluate the remaining lifetime of high voltage metallic towers, continuous inspections are required. These inspections are done today by experts climbing the towers, which is not only risky and time-consuming, but also costly and limited (not all parts of the towers can be reached). Hence, it is very important to develop an alternative inspection method to properly assess the repair these towers needs and extend their lifetimes. Imaging from drones is considered as a safe and fast tool for monitoring the environment and assets. However, using RGB imaging, it is not possible to distinguish between various types of corrosions, some of which can lead to severe material loss and thus compromised structural integrity. Moreover, RGB based image analysis is prone to false positives. Although hyperspectral imaging solves some of those problems, varying illumination conditions outdoor and the complex geometry of the towers bring extra challenges, which are addressed in the methodology developed in this paper. We designed a drone payload integrating both a LiDAR scanner and hyperspectral sensors. Leveraging reference spectra captured during a manual initialization step and extra features such as high dynamic range (HDR) for hyperspectral image acquisition resulted in a faster, less complex, and more reliable corrosion inspection.