scholarly journals Urban Traffic Density Estimation Based on Ultrahigh-Resolution UAV Video and Deep Neural Network

2018 ◽  
Vol 11 (12) ◽  
pp. 4968-4981 ◽  
Author(s):  
Jiasong Zhu ◽  
Ke Sun ◽  
Sen Jia ◽  
Qingquan Li ◽  
Xianxu Hou ◽  
...  
Keyword(s):  
Neural Network ◽  
Density Estimation ◽  
Deep Neural Network ◽  
Urban Traffic ◽  
Traffic Density ◽  
Ultrahigh Resolution

A Wave-Shaped Deep Neural Network for Smoke Density Estimation

2020 ◽  
Vol 29 ◽  
pp. 2301-2313 ◽  
Author(s):  
Feiniu Yuan ◽  
Lin Zhang ◽  
Xue Xia ◽  
Qinghua Huang ◽  
Xuelong Li
Keyword(s):  
Neural Network ◽  
Density Estimation ◽  
Deep Neural Network ◽  
Smoke Density

scholarly journals Pedestrian and cyclist detection based on deep neural network fast R-CNN

2019 ◽  
Vol 16 (2) ◽  
pp. 172988141982965 ◽  
Author(s):  
Kelong Wang ◽  
Wei Zhou
Keyword(s):  
Neural Network ◽  
Target Detection ◽  
Deep Neural Network ◽  
Detection Method ◽  
Feature Fusion ◽  
Intelligent Vehicles ◽  
Urban Traffic ◽  
Joint Detection ◽  
Difficult Case ◽  
Important Basis

In this article, a unified joint detection framework for pedestrian and cyclist is established to realize the joint detection of pedestrian and cyclist targets. Based on the target detection of fast regional convolution neural network, a deep neural network model suitable for pedestrian and cyclist detection is established. Experiments for poor detection results for small-sized targets and complex and changeable background environment; various network improvement schemes such as difficult case extraction, multilayer feature fusion, and multitarget candidate region input were designed to improve detection and to solve the problems of frequent false detections and missed detections in pedestrian and cyclist target detection. Results of experimental verification of the pedestrian and cyclist database established in Beijing’s urban traffic environment showed that the proposed joint detection method for pedestrians and cyclists can realize the stable tracking of joint detection and clearly distinguish different target categories. Therefore, an important basis for the behavior decision of intelligent vehicles is provided.

Sign in / Sign up

Export Citation Format

Share Document