[2021-12-14]: Training data augmentation via horizontal_flipping.py. Evaluation: first, converting ground truth xml to txt by get_gt_txt.py; Second, appling AP50-5-95.py to evaluate the detection results.

[2021-11-23]: A kaggle competition platform is built, then you can submit you result in https://www.kaggle.com/c/pvelad, and evaluate your algorithm.

Dataset application website: http://aihebut.com/col.jsp?id=118 or https://github.com/binyisu/PVEL-AD

2021 Dataset Access Instructions

We build a Photovoltaic Electroluminescence Anomaly Detection dataset (PVEL-AD ) for 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 such as crack (line and star), finger interruption, black core, misalignment, thick line, scratch, fragment, corner, and material defect. Moreover, 37,380 ground truth bounding boxes are provided for 8 types of defects.

The PVELAD-2021 Datasets Request Form is available here.

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 [email protected]

• The copyright of PVELAD dataset belongs to Hebei University of Technology.

[1] Binyi Su, Zhong Zhou, Haiyong Chen, “PVEL-AD: A Large-Scale Open-World Dataset for Photovoltaic Cell Anomaly Detection,” IEEE Trans. Ind. Inform., DOI (identifier) :10.1109/TII.2022.3162846

[2] B. Su, H. Chen, Y. Zhu, W. Liu and K. Liu, ``Classification of Manufacturing Defects in Multicrystalline Solar Cells With Novel Feature Descriptor,'' IEEE Trans. Instrum. Meas., vol. 68, no. 12, pp. 4675--4688, Dec. 2019.

[3] B. Su, H. Chen, and P. Chen, ``Deep Learning-Based Solar-Cell Manufacturing Defect Detection With Complementary Attention Network,'' IEEE Trans. Ind. Inform., vol. 17, no. 6, pp. 4084--4095, Jun. 2021.

[4] B. Su, H. Chen, and Z. Zhou, ``BAF-Detector: An Efficient CNN-Based Detector for Photovoltaic Cell Defect Detection,'' IEEE Trans. Ind. Electron., vol. 69, no. 3, pp. 3161-3171, Mar. 2022.