Applying Object Recognition to Improve Image Retrieval by Color Features

Marinov, Miroslav; Kalmukov, Yordan; Valova, Irena

doi:10.1109/infoteh60418.2024.10495955

2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH) 2024

DOI: 10.1109/infoteh60418.2024.10495955

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Applying Object Recognition to Improve Image Retrieval by Color Features

Miroslav Marinov,

Yordan Kalmukov,

Irena Valova

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2024

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PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning

Evstatiev,

Trifonov,

Gabrovska-Evstatieva

et al. 2024

Energies

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During the last decades photovoltaic solar energy has continuously increased its share in the electricity mix and has already surpassed 5% globally. Even though photovoltaic (PV) installations are considered to require very little maintenance, their efficient exploitation relies on accounting for certain environmental factors that affect energy generation. One of these factors is the soiling of the PV surface, which could be observed in different forms, such as dust and bird droppings. In this study, visible spectrum data and machine learning algorithms were used for the identification of soiling. A methodology for preprocessing the images is proposed, which puts focus on any soiling of the PV surface. The performance of six classification machine learning algorithms is evaluated and compared—convolutional neural network (CNN), support vector machine (SVM), random forest (RF), k-nearest neighbor (kNN), naïve-Bayes, and decision tree. During the training and validation phase, RF proved to be the best-performing model with an F1 score of 0.935, closely followed by SVM, CNN, and kNN. However, during the testing phase, the trained CNN achieved the highest performance, reaching F1 = 0.913. SVM closely followed it with a score of 0.895, while the other two models returned worse results. Some results from the application of the optimal model after specific weather events are also presented in this study. They confirmed once again that the trained convolutional neural network can be successfully used to evaluate the soiling state of photovoltaic surfaces.

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PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning

Evstatiev,

Trifonov,

Gabrovska-Evstatieva

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

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Applying Object Recognition to Improve Image Retrieval by Color Features

Cited by 1 publication

References 15 publications

PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning

PV Module Soiling Detection Using Visible Spectrum Imaging and Machine Learning

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