Browsing by Author "Rocha, Daniel"
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- A Deep Learning Approach for PV Failure Mode Detection in Infrared Images: First InsightsPublication . Rocha, Daniel; Lopes, Miguel; Teixeira, Jennifer P.; Fernandes, Paulo A.; Morais, Modesto; Salome, Pedro M. P.Large-scale solar power plants require cheap and quick inspections, for this unmanned aerial vehicle (UAV's) for high resolution optical and infrared imaging were introduced in the past years. While using UAV’s is fast for image acquisition, image is a time-consuming process where the best of practice today is still for an expert to individually analyze each image. As such, in this work we use computer vision to accelerate this process. We performed an instance segmentation assessment using a pretrained mask R-CNN for the segmentation of defective modules, and cells, as well as for segmentation and classification of failures. This method was chosen due its good past performance. In this work we created a database from a solar power plant consisting of 42048 modules and an expert analyzed the images. Later on, our computer algorithm results were benchmarked against the expert. Our algorithm achieved a mean average precision (mAP) in defective module segmentation mask of 72.1 % and 47.9 % in segmentation mask of failure type with an intersection over union threshold (IoU) of 0.50, without human interference. The presented preliminary results allow to assess the methodology advantages and drawbacks to increase performance and pave the way to a large-scale study.
- Multidefect detection tool for large-scale PV plants: Segmentation and classificationPublication . Rocha, Daniel; Alves, Joao; Lopes, Vitor; Teixeira, Jennifer P.; Fernandes, Paulo A.; Costa, Mauro; Morais, Modesto; Salome, Pedro M. P.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.