Computer Vision
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An approach for Intelligent detection of dirty PV panels using image processing with YOLOv8
Abstract: The efficiency of solar photovoltaic (PV) systems is greatly affected by the condition of panel surfaces, as dust buildup and other impurities can noticeably reduce power output. Traditional maintenance methods rely largely on manual inspection, which can be subjective, inconsistent, and impractical for large-scale installations. To address these challenges, this work investigates a deep learning-based computer vision approach for the automatic assessment of solar panel cleanliness. In this work, a …
Published in International Journal of Renewable Energy and its Commercialization · Vol. 12, Issue 2, 2026 Read article
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Development and Fabrication of Capsees
Abstract: Although the development of tools for visually impaired individuals is not a new problem, the field of computer-aided tools is still evolving. The primary objective of these systems is to assist users in independent navigation without relying on a second person. While there are existing works utilizing computer vision techniques, none of them can address all the fundamental needs of blind individuals. Current systems are typically designed for specific purposes, …
Published in International Journal of Computer Aided Manufacturing · Vol. 9, Issue 2, 2023 · pp. 25–33 Read article
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Intelligent Gear Inspection: Defect Detection through Digital Image Processing Algorithms
Abstract: A significant contributor to poor reliability and a source of humiliation for businesses are gear problems. The majority of the inspection procedures used in these businesses are laborious and manually. More thorough and accurate inspection procedures are needed to improve accuracy in finding gear problems. The present work fills this gap by implementing a Gear Defect Recognizer that uses local thresholding in conjunction with computer vision methodology to spot potential …
Published in International Journal of Computer Aided Manufacturing · Vol. 9, Issue 1, 2023 · pp. 13–21 Read article