Aiming at the current PV panel defect detection methods with insufficient accuracy, few defect categories, and the problem that defect targets cannot be localized, this paper proposes a PV panel
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
Advances in automation, prediction, and management have enabled sophisticated fault detection methods to enhance system reliability and availability. This paper emphasizes the pivotal
To address the low operational efficiency of detection algorithms and the low accuracy due to the similarity and large-scale variance of PV defects, we propose an improved lightweight
With the rapid development of photovoltaic technology, efficient and accurate defect detection in solar panels has become crucial for maintaining energy conversion efficiency and
Precise characterization of losses and effective fault detection are crucial for informed decision-making in PV system optimization. This work introduces a computational model for
We hypothesize that a lightweight object detection framework that integrates hierarchical feature enhancement and adaptive spatial processing can simultaneously improve the detection
This study not only offers a new, efficient, and accurate approach for PV defect detection but also provides strong technical support for intelligent operation and maintenance as well as quality
Photovoltaic (PV) systems play a vital role in the global transition to renewable energy, yet their efficiency is often compromised by surface defects such as dust accumulation, bird droppings,...
Automated defect detection is critical for addressing these challenges in large-scale solar farms, where manual inspections are impractical. This study evaluates three YOLO object detection
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