Defect-Detection-Classifier

Defect detection of water-cooled wall. The sample is hard to collect, so we only have a little dataset 
which includes 320 training images(160 normal+ 160 defect) and 80 testing images(40 normal+ 40 defect). 
The image size is 256*256. The dataset is collected by Dong Jin. Thanks the advice from Yu Fang about 
the using of gcForest.

dataset

the above three images are normal examples and the below are defect.

Classifier

We use Support Vector Machine(SVM) with different feature extractors, deep forest and Convolutional Neural 
Network to train the classifier.

• Gauss filter+LBP+SVM(rbf kernel)

  Use Gaussian filter and laplacian operator to denoise and extracts edges, then LBP(Local Binary Patt-
  ern) extract features of preprocessed images as the input of SVM.
  

• CNN+SVM(rbf kernel)

  Use VGG16 to extract features as the input of SVM., the weight of VGG16 is trained on ImageNet.
  

• simple CNN(3 Conv+1 FC)

  Build a simple neural network to train. The network consists of three convolutional layers and a fully
  connected layer.
  

• transfer Learning(VGG16)

  Use VGG16 to extract features as input of a simple network that consists of a fully-connected layer.
  

• Neural Network Search

  Use NNS to search a best network.
  

• gcForest

  Use deep forest(Only cascade forest structure/With multi-grained forests) to train the ensemble classifier. 
  

Result

classifier	accuracy
Gauss filter+LBP+SVM(rbf kernel)	97.25%
CNN+SVM(rbf kernel)	71.25%
simple CNN(3 Conv+1 FC)	72.50%
transfer Learning(VGG16)	81.25%
Neural Network Search	82.28%
gcForest (without multi-grained forests)	80.00%
gcForest (with multi-grained forests, i=8)	88.75%

run

Dependencies

• gcForest
  

• AutoKeras
  Currently, Auto-Keras is only compatible with: Python 3.6. And we need to install the depedencies under python3. [2019.5.10]

• others

  pip install -r requirements.txt
  

run

# read README.md in models folder and download weight file of pre-trained VGG on the ImageNet dataset.
# dataset
cp -rf normal_add/* ./normal
rm -rf normal_add/
cp -rf defect_add/* ./defect
rm -rf defect_add
# CNN+SVM(rbf kernel)
python cnnSVM.py
# simple CNN(3 Conv+1 FC)
python CNNclassifier.py
# transfer Learning(VGG16)
python transferLearning.py
# gcForest (without multi-grained forests) 
python ./data/train/write_label.py
python ./data/test/write_label.py
python ./gcForest/demo_Defect-Detection-Classifier.py --model ./gcForest/demo_Defect-Detection-Classifier-ca.json
# gcForest (with multi-grained forests, i=8) 
python ./gcForest/demo_Defect-Detection-Classifier.py --model ./gcForest/demo_Defect-Detection-Classifier-gc8.json
# Neural Network Search, copy autokeras dir to current path after it is installed from source.
python ./data/train/write_label2.py
python ./data/test/write_label2.py
python3 autoCNNclassifier.py

reference

scikit-learn tutorial
Building powerful image classification models using very little data