An effective approach to improving photovoltaic defect detection using
By addressing real-world challenges in solar panel maintenance, the final dataset supports applications in automated defect detection, predictive maintenance, and energy optimization.
A photovoltaic panel defect detection framework enhanced by deep
In recent years, with the rapid advancement of computer vision, deep learning-based object detection algorithms have offered new approaches and solutions for PV panel defect detection.
Fault Detection and Classification for Photovoltaic Panel System Using
To tackle these issues, a new machine-learning model will be presented. This model can accurately identify and categorize defects by analyzing various fault types and using electrical and
Enhanced photovoltaic panel defect detection via
Detecting defects on photovoltaic panels using electroluminescence images can significantly enhance the production quality of these panels.
A novel deep learning model for defect detection in photovoltaic
This study utilizes a publicly available visible light imaging dataset from Kaggle, which includes a large number of images of everyday solar PV panels taken with regular cameras,
A review of automated solar photovoltaic defect detection systems
The adoption of each of the reviewed techniques depends on several factors including the deployment scale, the targeted defects for detection, and the required location of defect analysis in
EBBA-detector: An effective detector for defect detection in solar
Solar panel defect detection, a crucial quality control task in the manufacturing process, often faces challenges such as varying defect sizes, severe image background interference, and
LEM-Detector: An Efficient Detector for Photovoltaic Panel Defect
To address these challenges, this paper proposes the LEM-Detector, an efficient end-to-end photovoltaic panel defect detector based on the transformer architecture.
Solar Panel Surface Defect and Dust Detection: Deep Learning
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,
Photovoltaic Panels Defect Detection Based on an Improved
In order to tackle this issue, this study presents a PV panel defect detection approach based on the advanced YOLOv11 object detection algorithm. The mosaic augmentation approach is first employed