DeepCrack: A Deep Hierarchical Feature Learning Architecture for Crack Segmentation, Neurocomputing.
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Updated
May 8, 2023
DeepCrack: A Deep Hierarchical Feature Learning Architecture for Crack Segmentation, Neurocomputing.
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.
This repo contains customized deep learning models for segmenting cracks.
Official PyTorch implementation of the paper "Joint Learning of Blind Super-Resolution and Crack Segmentation for Realistic Degraded Images", IEEE Transactions on Instrumentation and Measurement (TIM) 2024. CSBSR is an advanced version of our previous work CSSR [MVA'21].
Official code for ICIP 2023 paper "A Convolutional-Transformer Network for Crack Segmentation with Boundary Awareness"
🛣️🔍 | Road crack segmentation using PyTorch
HCMUS Team at SHREC22: Crack and Pothole Segmentation
DeepCrack + Colab [DeepSegmentor] Crack and Road detection based on IA web app
Segment the crack from walls with computer vision
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