PVEL-AD

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Photovoltaic cell defect detection

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AP50-5-95.py
EL2021.png
get_gt_txt.py
horizontal_flipping.py
Industrial_Data_Access_Form.docx
LICENSE
pvel.jpg
README.md

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# Solar cell EL image defect detection dataset（PVEL-AD）
**由于数据集受到大量关注和引用，应多数人要求，现将test标签一并公开，望大家周知！**
**Due to the growing interest and widespread usage of this dataset, and in response to numerous requests from the community, we have decided to publicly release the test set annotations as well.**
# News
[2026-1-30]：数据集网盘下载地址【new】**google drive**：[https://drive.google.com/file/d/1EtteKnLhSFQ3XMCRXt5wKY-lDkIP7299/view?usp=drive_link](https://drive.google.com/drive/folders/1AMlo433v-torspIxynzx0wXGced8Eo3q?usp=drive_link)

[2023-12-19]：**2周**内回复！Reply within **2 weeks**! I am busy with my graduation thesis, please understand!

[2023-03-06]：**所有人须知**: 我们没有提供测试标注，测试你的算法请到 https://www.kaggle.com/competitions/pvelad

[2023-03-06]：**Notice to all**: We do not provide test annotations, please go to the following URL for testing your algorithms https://www.kaggle.com/competitions/pvelad

[2022-04-13]：Box annotations for vertical_dislocation and horizontal_dislocation will be added into PVELAD dataset.

[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 cells, which contains 36,543 near-infrared images with various internal defects and heterogeneous backgrounds. This dataset contains 1 class of anomaly-free images and anomalous images with **12** different categories such as crack (line and star), finger interruption, black core, thick line, scratch, fragment, corner, printing_error, horizontal_dislocation, vertical_dislocation, and short_circuit defects. Moreover, 40358 ground truth bounding boxes are provided for **12** types of defects. This is a **long-tail object detection task**, which is challenging and significant for smart manufacturing.

| Category (12 classes)  | trainval |            test            |  
|------------------------|:--------:|:--------------------------:|
| finger                 |   2958   |            22638           |
| crack                  |   1260   |            2797            |    
| black_core             |   1028   |            3877            | 
| thick_line             |   981    |            1585            |
| horizontal_dislocation |   798    |            1582            |
| short_circuit          |   492    |            1215            |
| vertical_dislocation   |   137    |            271             |
| star_crack             |   135    |            83              |
| printing_error         |   32     |            48              |
| corner                 |    9     |            12              | 
| fragment               |    7     |            5               | 
| scratch                |    5     |            3               |

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 **Industrial 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 **Industrial Datasets Request Form** to subinyi@vip.qq.com 
* Note that If you want to download through google disk, please send me your **google email**.
* The dataset is jointly released by **Hebei University of Technology** and **Beihang University**.

![image](https://github.com/binyisu/PVEL-AD/blob/main/EL2021.png)

[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.

PVEL-AD

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