Vehicle Speed Prediction with Convolutional Neural Networks for ITS

2020 ◽  
Author(s):  
Yifei Li ◽  
Celimuge Wu ◽  
Tsutomu Yoshinaga
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Vehicle Speed ◽  
Speed Prediction

Traffic speed prediction under weekday using convolutional neural networks concepts

2017 ◽  
Author(s):  
Changhee Song ◽  
Heeyun Lee ◽  
Changbeom Kang ◽  
Wonyoung Lee ◽  
Young B. Kim ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Speed Prediction ◽  
Traffic Speed

Vehicle Speed Monitoring using Convolutional Neural Networks

2019 ◽  
Vol 17 (06) ◽  
pp. 1000-1008 ◽  
Author(s):  
V. Barth ◽  
R. de Oliveira ◽  
M. de Oliveira ◽  
V. do Nascimento
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Vehicle Speed

scholarly journals Vehicle Speed Detection and Safety Distance Estimation Using Aerial Images of Brazilian Highways

2020 ◽  
Author(s):  
Mateus Eloi da Silva Bastos ◽  
Vitor Yeso Fidelis Freitas ◽  
Richardson Santiago Teles De Menezes ◽  
Helton Maia
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Distance Estimation ◽  
Mean Average Precision ◽  
Aerial Images ◽  
Vehicle Speed ◽  
Safe Distance ◽  
Training Set ◽  
Average Precision ◽  
Safety Distance

In this study, the computational development conducted was based on Convolutional Neural Networks (CNNs), and the You Only Look Once (YOLO) algorithm to detect vehicles from aerial images and calculate the safe distance between them. We analyzed a dataset composed of 896 images, recorded in videos by a DJI Spark Drone. The training set used 60% of the images, 20% for validation, and 20% for the tests. Tests were performed to detect vehicles in different configurations, and the best result was achieved using the YOLO Full-608, with a mean Average Precision(mAP) of 95.6%. The accuracy of the results encourages the development of systems capable of estimating the safe distance between vehicles in motion, allowing mainly to minimize the risk of accidents.

scholarly journals Traffic flow speed prediction on transportation graph with convolutional neural networks

2018 ◽  
Vol 10 (3) ◽  
pp. 359-367 ◽  
Author(s):  
Nikolay G. Prokoptsev ◽  
Andrey Evgen'evich Alekseenko ◽  
Yaroslav Aleksandrovich Kholodov
Keyword(s):  
Neural Networks ◽  
Traffic Flow ◽  
Convolutional Neural Networks ◽  
Flow Speed ◽  
Speed Prediction

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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