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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Updated
May 6, 2024 - Python
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
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
Crack Analysis Tool in Python (CrackPy) - automatic detection and fracture mechanical analysis of (fatigue) cracks using digital image correlation
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.
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.
A pre-trained MobileNet model for detecting cracks on concrete structures
Crack detection for concrete structures
finding cracks in highway using some pattern recognition and machine learning methods.
A pre-trained MobileNet model for detecting cracks on concrete structures.
Here road crack detection was done using CNN with a large dataset.
This repository contains some SIMPLE modules for Crack Detection and Semantic Segmentation.
Expandable crack detection for composite materials. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021001205
Wall wear segmentation using Convolutional Neural Networks
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