Wall wear segmentation using Convolutional Neural Networks
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Updated
May 27, 2018 - Python
Wall wear segmentation using Convolutional Neural Networks
A pre-trained MobileNet model for detecting cracks on concrete structures.
Incorporating Inductive Bias into Deep Learning: A Perspective from Automated Visual Inspection in Aircraft Maintenance
A pre-trained MobileNet model for detecting cracks on concrete structures
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.
finding cracks in highway using some pattern recognition and machine learning methods.
Expandable crack detection for composite materials. To cite this Original Software Publication: https://www.sciencedirect.com/science/article/pii/S2352711021001205
This repo contains customized deep learning models for segmenting cracks.
Real time crack segmentation using PyTorch, OpenCV and ONNX runtime
Crack Detection On Highway Or Pavement Using OpenCV
A Pytorch implementation of DeepCrack and RoadNet projects.
Crack detection for concrete structures
Here road crack detection was done using CNN with a large dataset.
DeepCrack: Learning Hierarchical Convolutional Features for Crack Detection
This repository contains some SIMPLE modules for Crack Detection and Semantic Segmentation.
Crack Analysis Tool in Python (CrackPy) - automatic detection and fracture mechanical analysis of (fatigue) cracks using digital image correlation
This repository contains code and dataset for the task crack segmentation using two architectures UNet_VGG16, UNet_Resnet and DenseNet-Tiramusu
Official code for ICIP 2023 paper "A Convolutional-Transformer Network for Crack Segmentation with Boundary Awareness"
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.
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