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This study introduces an automated defect detection pipeline that leverages deep learning and computer vision to identify five standard anomaly classes: Non-Defective, Dust, Defective, Physical Damage, and Snow on photovoltaic surfaces. To build a robust foundation, a heterogeneous dataset of 8973. . However, PV panels are prone to various defects such as cracks, micro-cracks, and hot spots during manufacturing, installation, and operation, which can significantly reduce power generation efficiency and shorten equipment lifespan. Therefore, fast and accurate defect detection has become a vital. .
Photovoltaic panel dust treatment p hematical model,and proposed cleaning mechanisms. Handy i formation to readers,engineers,and practitioners. The main objective of this work was to pinpoint the. . The cleaning mechanism has been designed to clean the panel by controlling the R-F Circuits. Key Words: Rolling brush, R-F Circuit board, DC Gear motor, Design, Wheels. Traditionally cleaning system was done manually. This might also serve its role as a comparative analysis on the electro static cleaning and super-hydrophobic anti-soiling coating methods that are. . In tropical and humid climate, solar panel accumulates dirt owing to dust and moisture. The use of a water sprinkler before the wiper. .