DEye (Keep an Eye on Defects Inspection)

1. Abstract

Defect Eye is an open source software library based on tensorflow1.4, which focus on surface defect inspection. The application area cover the full range of yield applications within the manufacturing environment, including incoming process tool qualification, wafer qualification, glass surface qualification, reticle qualification, research and development, and tool, process and line monitoring. Patterned and unpatterned wafer defect inspection and qualification tools find particles and pattern defects on the front surface, back surface and edge of the wafer, allowing engineers to detect and monitor critical yield excursions. Also, It can be used for medical image inpsection, including Lung PET/CT,breast MRI, CT Colongraphy, Digital Chest X-ray images.

2. Usage

Compiled tensorflow-r1.4 GPU version using CMake,VisualStudio 2015, CUDA8.0, cudnn6.0.

tensorflow.dll, tensorflow.lib, libprotobuf.lib
Download Address: Link：https://pan.baidu.com/s/1o9tv1n8 password：ekec

How to use DEye

Install VisualStudio Community2015 Install NVIDIA CUDA 8.0
git clone https://github.com/sundyCoder/DEye
Download tensorflow.dll, place it under DEye/bin
Download tensorflow.lib and libprotobuf.lib, place theme under DEye/extra/tensorflow-r1.4/
Download inception_v3_2016_08_28_frozen.pb, place it under DEye/data
Open Visual Studio Solution "DEye.sln" which should be under DEye/build/vc14，Solution configurations option choose "Release", Soluton Platform option choose "x64".
Build and run the GUI project, you can do model training for your inspection cases.

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/

5. Contact

Email: sundycoder@gmail.com
QQ:    1316501606

Welcome to support money for future open source development.

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

6. License

Apache License 2.0

deebrillianttech/defects