Aiming at the three typical defects commonly found on the surface of photovoltaic (PV) panels, namely, shading, glass breakage and hot spots, a surface defect detection model (LW-PV
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Ensuring the quality of photovoltaic cells is paramount for enhancing the efficiency of solar energy systems. Traditional defect detection methods struggle with feature extraction and suffer from
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Using the Faulty Solar Panel Dataset, features were extracted via the InceptionV3 convolutional neural network and classified using Logistic Regression, Artificial Neural Networks and
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Traditional methods for photovoltaic panel defect detection primarily rely on manual visual inspection or basic optical detection equipment, both of which have significant limitations.
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Within this research, we introduce a streamlined yet effective model founded on the “You Only Look Once” algorithm to detect photovoltaic panel defects in intricate settings.
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This module is seamlessly integrated into YOLOv5 for detecting defects on photovoltaic panels, aiming primarily to enhance model detection performance, achieve model lightweighting, and...
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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,
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Developing efficient surface contaminants and defect detection algorithms for PV panels can facilitate automated and intelligent maintenance by robotic systems in large-scale PV power
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Recent advancements in machine vision, computer vision, and image processing have driven significant research into automated detection of surface defects in in PV panels.
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