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.
the above three images are normal examples and the below are defect.
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.
| classifier | accuracy |
|---|---|
| Gauss filter+LBP+SVM(rbf kernel) | 97.25% |
| CNN+SVM(rbf kernel) | 73.75% |
| simple CNN(3 Conv+1 FC) | 68.75% |
| transfer Learning(VGG16) | 82.50% |
| Neural Network Search | 82.28% |
| gcForest (without multi-grained forests) | 77.50% |
| gcForest (with multi-grained forests, i=8) | 88.75% |
- Keras
sudo pip install keras - NumPy
sudo pip install numpy - h5py
sudo pip install h5py - scikit-learn
sudo pip install scikit-learn - gcForest
- AutoKeras
compile from source code and revise according to this issue
# 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
cd
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
python ./data/train/write_label2.py
python ./data/test/write_label2.py
python autoCNNclassifier.py
scikit-learn tutorial
Building powerful image classification models using very little data
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