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CCNUZFW/PV-Multi-Defect: PV panel surface-defect detection
607 open source Defects images and annotations in multiple formats for training computer vision models. solar-panel-defects (v4, 2025-07-02 5:41pm), created by Defect detection in solar
Optimized YOLO based model for photovoltaic defect detection in
In this study, PV-YOLOv12n is introduced as an optimized variant of YOLOv12n, tailored for defect detection in electroluminescence (EL) images of PV panels.
solar-panel-defects Object Detection Model (v4, 2025-07-02 5:41pm)
607 open source Defects images and annotations in multiple formats for training computer vision models. solar-panel-defects (v4, 2025-07-02 5:41pm), created by Defect detection in solar
A novel deep learning model for defect detection in photovoltaic
The dataset originates from the Kaggle platform, specifically from the Solar Panel Images collection, which consists entirely of publicly available data. For this study, a dataset was compiled
Classification and Early Detection of Solar Panel Faults with Deep
This paper presents an innovative approach to detect solar panel defects early, leveraging distinct datasets comprising aerial and electroluminescence (EL) images.
Vision-Based Object Detection for UAV Solar Panel Inspection Using
A custom dataset, annotated in the COCO format and specifically designed for solar panel defect and contamination detection, was developed alongside a user interface to train and evaluate the models.
SPHERE: Benchmarking YOLO vs. CNN on a Novel Dataset for High
This study compares deep learning models for classifying solar panel images (broken, clean, and dirty) using a novel, proprietary dataset of 6079 images augmented to enhance performance.
A benchmark dataset for defect detection and classification in
In this current work, the labelled dataset is made public and the results from twelve deep learning models are compared and summarized to identify models that might be better suited for
PV Panel Defect Dataset
This dataset contains labeled images of photovoltaic (PV) panels across 6 defect classes. The dataset was created as part of an educational and research project to compare
Solar Panel Object Detection Dataset by TENSRAI
Detection of Unauthorized Solar Installations: In regions where regulations require licenses for solar panel installations, authorities could use the model to detect unauthorized or unregistered
CCNUZFW/PV-Multi-Defect: PV panel surface-defect detection dataset
PV panel surface-defect detection dataset. Contribute to CCNUZFW/PV-Multi-Defect development by creating an account on GitHub.
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