Citation

Ismail, Muhammad Arif Farhan and Mohamad, Hafizal and Ahmad Khalid, Syafiq Ashraf and Abdul Rahman, Shahnurriman and Suhaimi, Nur Sabrina and Alias, Mohamad Yusoff (2025) Image Detection and Classification for Monitoring of Photovoltaic Panel Operation and Maintenance. In: 2025 Multimedia University Engineering Conference, MECON 2025, 21 July 2025 - 23 July 2025, Cyberjaya, Malaysia.

Text 10.pdf - Published Version Restricted to Repository staff only Download (1MB)

Abstract

The rapid growth of photovoltaic (PV) systems has necessitated efficient monitoring and maintenance to ensure their optimal performance. The project focuses on automating the process of identifying common defects, including soiling, bird droppings, fire damage, broken glass, and normal panel conditions. The methodology involves sourcing a diverse dataset of PV panel images, enhancing the dataset through augmentation, and annotating images for training the deep learning model. Advanced Python tools, such as Albumentations, OpenCV, and Roboflow, were utilized to create a robust dataset and prepare it for training. The YOLO framework, supported by additional Python libraries, was employed to develop and train the model. Model performance was evaluated using metrics such as accuracy, precision, recall, F1 score, and confusion matrix.

Item Type:	Conference or Workshop Item (Paper)
Uncontrolled Keywords:	Deep learning, PV, image detection
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) > TA1501-1820 Applied optics. Photonics
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	18 Mar 2026 08:21
Last Modified:	19 Mar 2026 02:19
URII:	http://shdl.mmu.edu.my/id/eprint/15590

Downloads

Edit (login required)