Enhancing Photovoltaic Panel Inspection using RGB Image-Based Detection and Image Processing Techniques

Suhaili Beeran Kutty; Mohamad Ad-Fadhil Musa; Murizah Kassim; Puteri Nor Ashikin Megat Yunus

doi:10.24191/jcrinn.v10i2.528

Enhancing Photovoltaic Panel Inspection using RGB Image-Based Detection and Image Processing Techniques

Authors

Suhaili Beeran Kutty Faculty of Electrical Engineering, Universiti Teknologi MARA Johor Branch, Pasir Gudang Campus, 81750 Masai, Johor, Malaysia.
Mohamad Ad-Fadhil Musa SHB Malaysia Automotive Appliance Sdn. Bhd, Johor, Malaysia.
Murizah Kassim Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia.
Puteri Nor Ashikin Megat Yunus Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia.

DOI:

https://doi.org/10.24191/jcrinn.v10i2.528

Keywords:

Photovoltaic Panel Defects, RGB Image Analysis, Image Processing, K-means Clustering, Canny Edge Detection

Abstract

Photovoltaic (PV) panels have become more common in recent years due to their numerous benefits. However, ensuring that PV panels function optimally and reliably is essential for maximizing their efficiency and durability. Identifying and addressing flaws in PV panels is vital to achieving this goal. While thermographic imaging is routinely utilized for defect identification, the potential for using RGB images for this purpose is virtually untapped. This paper intends to investigate using RGB images to recognize PV panel defects, proposing a methodology that integrates image processing techniques such as K-means clustering, Canny edge detection, and grayscale conversion. The results show that defects on PV panels may be successfully discovered by applying K-means clustering and Canny edge detection to RGB images with an accuracy of 90.66%. This study sheds light on improving defect identification practices in the PV industry.

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