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

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DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection

A Pytorch implementation of DeepCrack and RoadNet projects.

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

Crack Detection On Highway Or Pavement Using OpenCV

📅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

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

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

A comprehensive paper list of deep learning for crack detection, in terms of learning paradigms, generalizability and datasets.

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

This repo contains customized deep learning models for segmenting cracks.

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

Crack detection for concrete structure using Matlab

Training dataset for Crack Detection

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 images of defects against a pre-existing reconstructed 3D point cloud of the bridge. We also investigate methods of reducing computation time with ML-based and conventional CV methods of segmentation to eliminate redundant keypoints. Our project successfully localizes the defect images and achieves a savings in runtime from filtering keypoints.

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.

Detect road anomalies such as cracks, potholes, and bumps using our trained YOLOv8 models with visual demo. Real-time detection via Streamlit and Flask app

This is a Surface Crack Detection project implemented with the Tensorflow. We fine tuning some deep learning models (like VGG 19, VGG16, MobileNetV2, ...). Use Surface Crack Detection dataset available on kaggle.

hackaTUM2019

Python based application to detect and demarcate cracks in a photo of a concrete surface.

A Bayesian Convolutional Neural Network approach for image-based crack detection and maintenance applications

Concrete Crack Detection 🧱 https://ieeexplore.ieee.org/document/10693377

Road Crack Detection Using Deep Convolutional Neural Network and Adaptive Thresholding (IV'19)

A pre-trained MobileNet model for detecting cracks on concrete structures

Mask and instance-based crack detection for Python 3, Keras and TensorFlow 1.x.x

finding cracks in highway using some pattern recognition and machine learning methods.

Crack detection for concrete structures