Abstract
Investigators discuss new findings in . According to news reporting out of Orem, Utah, by NewsRx editors, research stated, "Traditionally, condition monitoring of wind turbines has been performed manually by certified rope teams. This method of inspection can be dangerous for the personnel involved, and the resulting downtime can be expensive." Financial supporters for this research include Office of The Commissioner of Utah System of Higher Education (Ushe)-deep Technology Initiative. The news reporters obtained a quote from the research from Utah Valley University: "Wind turbine inspection can be performed using autonomous drones to achieve lower downtime, cost, and health risks. To enable autonomy, the field of drone path planning can be assisted by this research, namely machine learning that detects wind turbines present in aerial RGB images taken by the drone before performing the maneuvering for turbine inspection. For this task, the effectiveness of two deep learning architectures is evaluated in this paper both without and with a proposed fuzzy contrast enhancement (FCE) image preprocessing algorithm. Efforts are focused on two convolutional neural network (CNN) variants: VGG19 and Xception. A more traditional approach involving support vector machines (SVM) is also included to contrast a machine learning approach with our deep learning networks. The authors created a novel dataset of 4500 RGB images of size 210 x 210 to train and evaluate the performance of these networks on wind turbine detection. The dataset is captured in an environment mimicking that of a wind turbine farm, and consists of two classes of images: with and without a small-scale wind turbine (12V Primus Air Max) assembled at Utah Valley University."
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