Photovoltaic cell anomaly detection dataset

Citation Author(s):
Binyi
Su
Zhong
Zhou
Haiyong
Chen
Hebei University of Technology
Submitted by:
Binyi Su
Last updated:
Tue, 03/15/2022 - 10:40
DOI:
10.21227/pz6t-3s77
License:
3.5
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Abstract 

The anomaly detection in photovoltaic (PV) cell electroluminescence (EL) image is of great significance for the vision-based fault diagnosis. Many researchers are committed to solving this problem, but a large-scale open-world dataset is required to validate their novel ideas. We build a PV EL Anomaly Detection (PVEL-AD) dataset for polycrystalline solar cell, which contains 36,543 near-infrared images with various internal defects and heterogeneous background. This dataset contains anomaly-free images and anomalous images with 10 different categories. Moreover, 37,380 ground truth bounding boxes are provided for 8 types of defects. We also carry out a comprehensive evaluation of the state-of-the-art object detection methods based on deep learning. The evaluation results on this dataset provide the initial benchmark, which is convenient for follow-up researchers to conduct experimental comparisons. To the best of our knowledge, this is the first public dataset for PV solar cell anomaly detection that provides box-wise ground truth and focuses on industrial application. Furthermore, this dataset can also be used for the evaluation of many computer vision tasks such as few-shot detection, one-class classification and anomaly generation. More details are presented in https://github.com/binyisu/PVEL-AD

Instructions: 

All researchers need to follow the instructions below to access the datasets.

  • Download and fill the Datasets Request Form (MUST be hand signed with date). Please use institutional email address(es). Commercial emails such as Gmail and QQmail are NOT allowed.

  • Email the signed Datasets Request Form to Subinyi@buaa.edu.cn

  • Annotation for boudning boxes are provided to train YOLO-v4-5/EfficientDet D1-7 based detectors

Comments

I can't find annotation (Ground truth) For testing dataset

Submitted by abdalluh aldulaimi on Sun, 02/05/2023 - 16:52

Documentation

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File README_PVELAD.md2.27 KB