YOLO EXPERT SYSTEM FOR REAL-TIME PATTERN RECOGNITION USING DRONES ON WIND FARM TURBINE

Ricardo Carreã‘o Aguilera, Marco A. Acevedo Mosqueda, Maria Elena Acevedo Mosqueda and Sandra Luz Gomez Coronel
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Ricardo Carreã‘o Aguilera: Universidad del Istmo – UNISTMO, Ciudad Universitaria S/N, Barrio Santa Cruz 4a. Sección Sto. Domingo Tehuantepec, C. P. 70760, Oaxaca, México
Marco A. Acevedo Mosqueda: ��Instituto Politécnico Nacional – SEPI ESIME Zacatenco, Unidad Profesional Adolfo López Mateos, Zacatenco. Alcaldía Gustavo A. Madero, C. P. 07738, Ciudad de México, México
Maria Elena Acevedo Mosqueda: ��Instituto Politécnico Nacional – SEPI ESIME Zacatenco, Unidad Profesional Adolfo López Mateos, Zacatenco. Alcaldía Gustavo A. Madero, C. P. 07738, Ciudad de México, México
Sandra Luz Gomez Coronel: ��Unidad profesional interdisciplinaria en ingeniería, y tecnologías avanzadas – UPIITA IPN, Avenida Instituto Politécnico Nacional No. 2580, Col. Barrio la Laguna Ticomán, C. P. 07340, Gustavo A. Madero, Ciudad de México, México

FRACTALS (fractals), 2025, vol. 33, issue 05, 1-9

Abstract: This research explores novel approaches integrating drone technology, artificial intelligence, and the Internet of Things to drive continuous innovation and increase operational efficiency. We developed an expert system employing the state-of-the-art YOLO deep learning framework for real-time recognition of physical defects in wind turbine blades from visual drone inspections. By leveraging YOLO’s single-shot detection methodology and optimized convolutional neural networks, our model analyzes images with unprecedented speed and precision without requiring computationally expensive region proposal algorithms. It identifies flaws instantly as the drone captures blade footage in motion. Significantly, this permits immediate pattern analysis of visible damage and continuously monitors blade integrity during active energy generation. Implementing our efficient YOLO-based system allows for automated, around-the-clock defect detection for optimized turbine maintenance and performance management.

Keywords: Drone Technology; Wind Farm; YOLO Deep Learning; Maintenance Internet of Things (IoT) (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0218348X25500471

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