DEye (Keep an Eye on Defects Inspection)

DEye 🚀 linux version is open sourced, please find the source code from the link: https://github.com/sundyCoder/DEye_linux

1. Abstract

Defect Eye (DEye) is a deep learning-based software for manufacturing surface defect inspection. It provides the basic function modules to facilitate the development of different defect inspection applications. The applications cover the full rang of manufacturing environment, including incoming process tool qualification, wafer qualification, glass surface qualification, reticle qualification, research and development. Also, It can be used for medical image inpsection, including Lung PET/CT,breast MRI, CT Colongraphy, Digital Chest X-ray images. This software library contains the basic function modules about data processing, model training and model inference. It is developed to reduce the burden of programmers who worked in this field. Based on this software, developers can design the added functions according to their requirements..

2. Usage

Compiled tensorflow-r1.4 GPU version using CMake,VisualStudio 2017, CUDA8.0, cudnn6.0.

tensorflow.dll, tensorflow.lib, libprotobuf.lib
Baidu Cloud Link：https://pan.baidu.com/s/1nCfPWbxv03FRaU4kip0yTQ password：jcbu
Google Drive Link: https://drive.google.com/open?id=1kANDNErMNLU9wNR3rKhUTz--ltwPNPUv

How to use DEye

Please find the user manual in the User Manual.

3. Applications

3.1 IC Chips Defects Inspection

3.2 Highway Road Crack Damage Inpection

3.3 Fabric Defects Inpection

3.4 Cover Glass Inpection

3.5 Civil Infrastructure Defect Detection

3.6 Power lines Crack Detection

3.7 Medical Image Classification

4. Datasets

Weakly Supervised Learning for Industrial Optical Inspection

DAGM: https://hci.iwr.uni-heidelberg.de/node/3616
Micro surface defect database

https://pan.baidu.com/s/1QM0AxlGjUlkHHyxwamIMmA
Oil pollution defect database

https://pan.baidu.com/s/1_aU_Bfh7lcxpYW1no2MlUQ
Bridge Crack Image Data

http://pan.baidu.com/s/1bplPrPl
ETHZ Datasets

ETHZ: http://www.vision.ee.ethz.ch/en/datasets/
RSDDs dataset

http://icn.bjtu.edu.cn/Visint/resources/RSDDs.aspx
Crack Forest Datasets

https://github.com/cuilimeng/CrackForest
CV Datasets on the Web

http://www.cvpapers.com/datasets.html
An RGB-D dataset and evaluation methodology for detection and 6D pose estimation of texture-less objects

http://cmp.felk.cvut.cz/t-less/
Pipes defect inspection dataset

https://vap.aau.dk/sewer-ml/

5. Contact

Email: sundycoder@gmail.com
QQ: 1316501606

Notice: Any comments and suggetions are welcomed, kindly please introduce yourself(name, country, organization etc.) when contact with me, thanks for your cooperation.

6. TODO List

A user-friendly GUI ( welcome to contact with me if you want to be a collaborator)

7. License

Apache License 2.0

8. Citation

Use this bibtex to cite the paper or this repository:

@article{li2022eid,
  title={EID-GAN: Generative Adversarial Nets for Extremely Imbalanced Data Augmentation},
  author={Li, Wei and Chen, Jinlin and Cao, Jiannong and Ma, Chao and Wang, Jia and Cui, Xiaohui and Chen, Ping},
  journal={IEEE Transactions on Industrial Informatics},
  year={2022},
  publisher={IEEE}
}

@misc{DEye,
  title={A Deep Learning-based Software for Manufacturing Defect Inspection},
  author={Sundy},
  year={2017},
  publisher={Github},
  journal={GitHub repository},
  howpublished={\url{https://github.com/sundyCoder/DEye}},
}

GLaStu/DEye