[2509.05348] Benchmarking CNN and Transformer-Based Object
Timely and accurate detection of defects and contaminants in solar panels is critical for maintaining the efficiency and reliability of photovoltaic (PV) systems.
Fault Detection in Solar Energy Systems: A Deep Learning Approach
This study explores the potential of using infrared solar module images for the detection of photovoltaic panel defects through deep learning, which represents a crucial step toward
An effective approach to improving photovoltaic defect
A custom dataset was constructed by combining a public PV panel defect database with field-collected images, further expanded through data
Defect Detection of Photovoltaic Panels to Suppress Endogenous
Abstract: Efficient and intelligent surface defect detection of photovoltaic modules is crucial for improving the quality of photovoltaic modules and ensuring the reliable operation of large-scale
RentadroneCL/Photovoltaic_Fault_Detector
In this repository you will find trained detection models that point out where the panel faults are by using radiometric thermal infrared pictures. In Web-API
Fault Detection and Classification for Photovoltaic
The deployment of solar photovoltaic (PV) panel systems, as renewable energy sources, has seen a rise recently. Consequently, it is
A photovoltaic panel defect detection framework
This paper proposes a photovoltaic panel defect detection method based on an improved YOLOv11 architecture. By introducing the CFA and
CHS-YOLO: enhanced lightweight YOLOv11 model for accurate
Real-time detection of photovoltaic panel defects remains highly challenging, as the model must simultaneously overcome algorithmic performance bottlenecks and background interference.
Automated detection and tracking of photovoltaic modules from 3D
This methodology has significant potential to improve the management, monitoring, and performance evaluation of photovoltaic solar panel installations, contributing to the advancement of
ST-YOLO: A defect detection method for photovoltaic
The adoption of a deep learning-based infrared image detection algorithm for PV modules significantly reduces the cost of manual inspection
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