dredwardlee,github

optimizedimageenhance

Several image/video enhancement methods, implemented by Java, to tackle common tasks, like dehazing, denoising, backscatter removal, low illuminance enhancement, featuring, smoothing and etc.

paper-tips-and-tricks

Best practice and tips & tricks to write scientific papers in LaTeX, with figures generated in Python or Matlab.

pavement-crack-dataset

We have published two new pavement crack image dataset, named Cics Dataset and Leis Dataset.

In this repository, two types of robust multi-scale anisotropy features, namely PCmPA and PCmFFA, that considering the local contextual information of curvilinear structures are extracted from phase congruency maps for the effective discrimination of isotropic structures and curvilinear structures.

pwc

Papers with code. Sorted by stars. Updated weekly.

pytorch-scenenetrgbd

Implementation of UNet as used in SceneNet RGB-D paper

pytorch_tutorial

《Pytorch模型训练实用教程》中配套代码

red-lesion-detection

This code implements a red lesion detection method based on a combination of hand-crafted features and CNN based descriptors. Our paper is under revision now, so please do not use this repository until we release the paper.

road-crack-detection

road-cracks-detection-neural-network-

A convolutional neural network built using keras to detect cracks on road with 97.5% accuracy.

road-damage-detection

Keeping roads in a good condition is vital to safe driving. To monitor the degradation of road conditions is one of the important component in transportation maintenance which is labor intensive and requires domain expertise. Automatic detection of road damage is an important task in transportation maintenance for driving safety assurance. The intensity of damage and complexity of the background, makes this process a challenging task. A deep-learning based methodology for damage detection is proposed in this project after being inspired by recent success on applying Deep- learning in Computer Sciences. A dataset of 9,053 images is taken with the help of a low cost smart phone and a quantitative evaluation is conducted, which in turn demonstrates that the superior damage detection performance using deep-learning methods perform extremely well when compared with features extracted with existing hand-craft methods. Using convolutional neural networks to train the damage detection model with our dataset, we use the state-of-the-art object detection method, and compute the accuracy and runtime speed on a GPU server. At the end, we show that the type of damage can be distinguished into eight types with acceptable accuracy by applying the proposed object detection method.