基于python实现CNN-SVM的硅片缺陷检测算法

## 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](https://github.com/kingfengji/gcForest) <br> * [AutoKeras](https://github.com/jhfjhfj1/autokeras) <br> 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](http://scikit-learn.org/dev/modules/generated/sklearn.svm.SVC.html) </br> [Building powerful image classification models using very little data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html)