taorui201828's Stars
JeremyOng96/A-Hybrid-Method-for-Pavement-Crack-Width-Measurement
A method to calculate the width of a binary image as described in:
nzpi/UNET_Concrete_Crack_Critical_Width_Detection
Python UNET implementation on the problem of concrete crack detection and critical width location
fengdu78/WZU-machine-learning-course
温州大学《机器学习》课程资料(代码、课件等)
dvalex/daunet
DAUNet: Deep Augmented Neural Network for Pavement Crack Segmentation
shreesha17/RoadPavementConditionPredictor
CNN model which will predict the severity of different distresses present in the given road image.
taorui201828/crack_width
This package contains programs written by Carrasco M, Araya-Letelier G, Velázquez R, Visconti P., for the implementation of the Image-Based Automated Width Measurement of Surface Cracking
taorui201828/DeepSegmentor
A Pytorch implementation of DeepCrack and RoadNet projects.
CodeSama346623/Bilibili346623
Examples shown in Bilibili Live 346623
JingyibySUTsoftware/Yolov5-deepsort-driverDistracted-driving-behavior-detection
基于深度学习的驾驶员分心驾驶行为(疲劳+危险行为)预警系统使用YOLOv5+Deepsort实现驾驶员的危险驾驶行为的预警监测
MadanMaram/Crack_segmentation
CrackDetection for both pavement and concrete meterials
rbgirshick/rcnn
R-CNN: Regions with Convolutional Neural Network Features
xiaochus/TrafficFlowPrediction
Traffic Flow Prediction with Neural Networks(SAEs、LSTM、GRU).
yhlleo/DeepCrack
DeepCrack: A Deep Hierarchical Feature Learning Architecture for Crack Segmentation, Neurocomputing.
ultralytics/yolov5
YOLOv5 🚀 in PyTorch > ONNX > CoreML > TFLite
florinsch/BigTrafficData
Traffic Forecasting In Complex Urban Networks: Leveraging Big Data and Machine Learning - Florin Schimbinschi, Xuan Vinh Nguyen, James Bailey, Chris Leckie, Hai Vu, Ramamohanarao Kotagiri
stxupengyu/PSO-RBF-NN
使用粒子群算法优化的RBF神经网络进行预测。RBF neural network optimized by particle swarm optimization is used for prediction.
taiwotman/Smart-Traffic
A system and method for the prediction of vehicle traffic congestion on a given roadway within a region. In particular, the computer implemented method of the present disclosure utilize real time traffic images from traffic cameras for the input of data and utilizes computer processing and machine learning to model a predictive level of congestion within a category of low congestion, medium congestion, or high congestion. By implementing machine learning in the comparison of exemplary images and administrator review, the computer processing system and method steps can predict a more efficient real-time congestion prediction over time.
Allan-Avila/Highway-Traffic-Dynamics-KMD-Code
LaxmiChaudhary/Modeling-of-strength-of-high-performance-concrete-using-Machine-Learning
JXQI/crack_Identify
水泥地裂纹识别
wwbweibo/RoudDetect
道路裂缝识别
Grootzz/GA-BP
基于遗传算法的BP网络设计,应用背景为交通流量的预测
xinchenstephen/stock_prediction
基于粒子群算法的神经网络优化股票价格预测
stxupengyu/BP-RBF-Prediction
使用BP神经网络、RBF神经网络以及PSO优化的RBF神经网络进行数据的预测
mratsim/McKinsey-SmartCities-Traffic-Prediction
Adventure into using multi attention recurrent neural networks for time-series (city traffic) for the 2017-11-18 McKinsey IronMan (24h non-stop) prediction challenge
TommyZihao/zihaopytorch
simple tutorial of pytorch
lipopo/traffic_flow_multi_model_predict
交通流量多模型预测
lsadouk/traffic_flow_code
short term traffic forecasting using deep learning architectures
super973/TrafficFlowPrediction
城市交通道路流量预测
bobbychovip/TrafficFlowPrediction
LCTFP: A freeway traffic flow prediction model based on CNN and LSTM