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Advisor(s)
Abstract(s)
Unmanned aerial vehicles (UAVs) with highresolution optical and infrared (IR) imaging have been introduced
in recent years to perform inexpensive and fast inspections in operation and maintenance activities of solar power plants, reducing the labor needed, while lowering the on-site inspection time. Even though UAVs can acquire images extremely quickly, the analysis of those images is still a time-consuming procedure that should be performed by a trained professional. Therefore, a computer vision approach may be used to accelerate image analysis. In this work, a dataset of IR images was created from a 10-MW solar power plant and a comparative analysis between mask R- convolutional neural network (CNN) and U-Net was performed for two experiments. Concerning the defective module segmentation, the mask R-CNN algorithm achieved a mean
average precision at intersection over union (IoU) = 0.50 of 0.96, using augmentation data. Regarding the segmentation and classification of failure type, the algorithm reached a value of 0.88 considering the same evaluation metric and data augmentation.When compared to the U-Net in terms of IoU, the mask R-CNN
outperformed it with 0.87 and 0.83 for the first and second experiments, respectively.
Description
Keywords
Class of abnormality Convolutional neural network (CNN) Failure mode Image classification Image segmentation Large-scale photovoltaic (PV) plant Thermographic inspection
Citation
D. Rocha et al., "Multidefect Detection Tool for Large-Scale PV Plants: Segmentation and Classification," in IEEE Journal of Photovoltaics, vol. 13, no. 2, pp. 291-295, March 2023, doi: 10.1109/JPHOTOV.2023.3236188
Publisher
IEEE