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crack-segmentation

U-Net CNN for segmenting cracks on road images.

crack-segmentation-1

crack-semantic-segmentation

Real time crack segmentation using PyTorch, OpenCV and ONNX runtime

crack_detection

VGG + Autoencoders + YOLO_v4 + Detectronn2 (Instance Segmentation)

crack_detection_detectron2

crack_detection_mask-rcnn

Cracks in concrete structures create lots of problems especially in old buildings; timely identification and curing the cracks which has occurred on concrete can help in increasing the life time of the buildings. We have develop a system to identify and spot crack(s) in civil structures.

crack_segmentation

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

cs543_project_image-based-localization-of-bridge-defects-with-ar-visualization

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.

cssr

Crack Segmentation for Low-Resolution Images using Joint Learning with Super-Resolution (CSSR in MVA2021)

ctlungsegmentationunet

Lung segmentation in CT volumes using 2D UNet. Huge thanks to Dennis Madsen channel on youtube!

cvpr2021-papers-with-code

CVPR 2021 论文和开源项目合集

d2go

D2Go is a toolkit for efficient deep learning

data-driven-advection

deep-learning-high-entropy-alloys

High Entropy Alloys (HEAs) are multi-chemical elements alloys with exceptional physical properties. HEAs have sparked the interest in engineering applications such as energy storage, catalysis and bio/plasmonic imaging. The understanding of the structural of composition of HEAs is paramount for the appropriate tuning of their properties. Scanning Transmission Electron Microscopy (STEM) is typically used to acquire images of various materials at the atomic scale resolution. including HEAs. In this repository it is demonstrated how computer vision analysis based on Deep Learning (DL) could be used to extract structural information from STEM images of HEAs. In particular a Fully Convolutional Neural Network (FCN) is trained to recognize the number of atoms of different chemical species in the atomic columns of HEA (i.e., column heights CHs) through semantic segmentation of simulated and experimental STEM images. As a benchmark case, equiatomic PtNiPdCoFe HEAs are considered. This project represent a first attempt for the identification of chemical species in 3D materials. Thus, in addition to the estimation of the structural properties of HEAs, this work establish an advancement of DL applied to microscopy image which could be useful for a broad area of nano-science applications.

deep-learning-hrtem-images-gold-nanoparticles

deep-learning-models

Keras code and weights files for popular deep learning models.

deepcrack

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

deepdeconvresunet

Landslide segmentation using Deep Deconvolutional Residual UNet

deepicedrain

Mapping and monitoring deep subglacial water activity in Antarctica using remote sensing and machine learning, with ICESat-2!

deeplearning-500-questions

深度学习500问，以问答形式对常用的概率知识、线性代数、机器学习、深度学习、计算机视觉等热点问题进行阐述，以帮助自己及有需要的读者。 全书分为18个章节，50余万字。由于水平有限，书中不妥之处恳请广大读者批评指正。 未完待续............ 如有意合作，联系[email protected] 版权所有，违权必究 Tan 2018.06

deepsem

A deep neural net approach to nanostructure analysis in scanning electron microscope (SEM) images.

defect-detection-of-pcb

A repository for creating a Deep Learning model for Defect Detection in PCBs and to design a Web Application for it.

detectron

FAIR's research platform for object detection research, implementing popular algorithms like Mask R-CNN and RetinaNet.

detectron2

In this repository, I have implemented several state-of-the-art computer vision tasks using Detectron 2.

detectron2-1

Detectron2 is FAIR's next-generation platform for object detection, segmentation and other visual recognition tasks.

detectron2tomobile

dl-for-satellite-image-analysis

This includes short and minimalistic few examples covering fundamentals of Deep Learning for Satellite Image Analysis (Remote Sensing).

dnn_magnet

Dataset and Architectures for a hysteresis model of a magnetorheological damper complement of the article entitle A Deep Neural Network based model for a kind of magnetorheological dampers.

doing_bayesian_data_analysis

Python/PyMC3 versions of the programs described in Doing bayesian data analysis by John K. Kruschke

drama

Main component extraction for outlier detection