This repository contains code and dataset for the task crack segmentation using two architectures UNet_VGG16, UNet_Resnet and DenseNet-Tiramusu

A Pytorch implementation of DeepCrack and RoadNet projects.

DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection

DeepCrack: A Deep Hierarchical Feature Learning Architecture for Crack Segmentation, Neurocomputing.

Crack Detection On Highway Or Pavement Using OpenCV

Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution (CSSR) was accepted to international conference on MVA2021 (oral), and selected for the Best Practical Paper Award.

Real time crack segmentation using PyTorch, OpenCV and ONNX runtime

Incorporating Inductive Bias into Deep Learning: A Perspective from Automated Visual Inspection in Aircraft Maintenance

This repo contains customized deep learning models for segmenting cracks.

Crack Analysis Tool in Python (CrackPy) - automatic detection and fracture mechanical analysis of (fatigue) cracks using digital image correlation

📅This repository contains the code for crack detection in concrete surfaces. It is a PyTorch implementation of Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks

CNN for crack classification, intended for use in a crack inspection pipeline (see references).

Crack detection for concrete structure using Matlab

Visual inspection of bridges is customarily used to identify and evaluate faults. However, current procedures followed by human inspectors demand long inspection times to examine large and difficult to access bridges. To address these limitations, we investigate a computer vision‐based approach that employs SIFT keypoint matching on collected im…

Training dataset for Crack Detection

Official code for ICIP 2023 paper "A Convolutional-Transformer Network for Crack Segmentation with Boundary Awareness"

A python-based crack detection and classification system using deep learning; used YOLO object detection algorithm. To extract the features of cracks we used Computer Vision and developed a desktop tool using Kivy to display the outcomes.

hackaTUM2019