sanwen211314 / cnn-for-crack-detection

An application of CNN for crack detection using Caffe

CNN-for-Crack-Detection

An application of CNN for crack detection using Caffe

Requirements

Caffe-GPU in Windows system (with compiled MATLAB interface)
MATLAB R2014a

Content

train/0/*: Folder for training images with cracks
train/1/*: Folder for training images without cracks
val/0/*: Folder for validation images with cracks
val/1/*: Folder for validation images without cracks
test/*: Folder for testing images and testing results
train_leveldb: Folder for converted training set
val_leveldb: Folder for converted validation set
train.txt: label file of training set
val.txt: label file of validation set
train_label.m: MATLAB code for generating train.txt
val_label.m: MATLAB code for generating val.txt
convert_train_leveldb.bat: Batch file for converting training set
convert_val_leveldb.bat: Batch file for converting validation set
train_mean.binaryproto: Mean file of training set
val_mean.binaryproto: Mean file of validation set
mean_train.bat: Batch file for computing mean of training set
mean_val.bat: Batch file for computing mean of validation set
train_val.prototxt: CNN architecture of training and validation processes
solver.prototxt: Solver file for setting training and validation parameters
train.bat: Batch file for training and validating the CNN
log.txt: Log file of training and validation processes
trained_models: Folder for saving trained CNN model
deploy.prototxt： Deploy file used in CNN testing process
demo/*.m: MATLAB codes for testing the trained model

Useage

Preraring datasets

1. Prepare your own data. Then put the data into train/ and val/ respectively
2. Generating label files train.txt and val.txt, run train_label.m and val_label.m
3. Converting training set and validation set to genarate train_leveldb and val_leveldb, run
   convert_train_leveldb.bat and convert_val_leveldb.bat
4. Computing means of training set and validation set to genarate train_mean.binaryproto and
   val_mean.binaryproto, run mean_train.bat and mean_val.bat


Training and validation

Run train.bat, then the log.txt will be created autometiclly and trained CNN models will be saved in the
trained_models folder


Testing

In the path of compiled MATLAB interface caffe/matlab/demo, run demo/AlexNet_test.m. Then testing results
will be saved in the test folder

Note

1. In all batch files (*.bat), the path of compiled caffe must be changed correctly 
2. The MATLAB files in demo folder must be run in the path of compiled MATLAB interface caffe/matlab/demo
3. In AlexNet_test.m, project_dir must be changed as the absolute path to the test folder