This is my Diploma Thesis ¨Road Crack Detection Using Deep Learning Methods¨ under the supervision of Dr.George Sfikas.

• Python
• LaTex

• Pytorch

• numpy
• sklearn
• PIL
• splitofolders
• torch
• time
• torchvision
• tqdm
• quaternion library

Road crack detection has vital importance in driving safety. However, it is very challenging because of the complexity of the background, cracks are easily confused with foreign objects, shadows, background textures and are also inhomogeneous. Many methods have been proposed for this task, but Convolutional Neural Networks(CNN) are promising for crack classification and segmentation with high accuracy and precision. Another method that is being studied is the use of quaternions. Quaternions have the advantage of providing more structural information of the color and as a result offer better learning results and avoid overfitting. In this study we focused on implementing various cnn networks and compare their results with Quaternion Convolutional Neural Networks (QCNN) which are an extension of the cnn in the quaternion domain for image classification and segmentation. Specifically we replaced the convolutional and linear layers of the cnn’s with quaternion convolutional layers and linear layers respectively. Deep features are learned directly from raw iamges.

Our Project has two components, the classification and the segmentation part. In the classification part we build neural networks such as Alexnet ,Vgg16 and a custom model and in the segmentation part we took an already implemented Deep Hierarchical Feature Learning Architecture for Crack Segmentation DeepCrack.

For the classification part we took images from here and here. For the segmentation part we took the dataset of the Deepcrack github repository

Quaternion DeepCrack model can be downloaded in this google drive. You can run the model by installing the quaternion library and then following the instructions in the github repository