Neural Network Detection of Objects of the Preset Type on the Road of the Car

2021 ◽  
pp. 197-203
Author(s):  
Oleg I. Fedyaev ◽  
Yaroslav A. Reshetnyak
Keyword(s):  
Neural Network ◽  
The Road ◽  
Detection Of Objects ◽  
On The Road

scholarly journals An Automatic Traffic Rules Violation Detection and Number Plate Recognition System for Bangladesh

2020 ◽  
Vol 19 (2) ◽  
pp. 87-98
Author(s):  
Raian Shahrear ◽  
Md. Anisur Rahman ◽  
Atif Islam ◽  
Chamak Dey ◽  
Md. Saniat Rahman Zishan
Keyword(s):  
Neural Network ◽  
Recognition System ◽  
The Road ◽  
Controlling System ◽  
Detection And Identification ◽  
Violation Detection ◽  
Computer Vision Technology ◽  
On The Road ◽  
Day By Day ◽  
Traffic Rules

The traffic controlling system in Bangladesh has not been updated enough with respect to fast improving technology. As a result, traffic rules violation detection and identification of the vehicle has become more difficult as the number of vehicles is increasing day by day. Moreover, controlling traffic is still manual. To solve this problem, the traffic controlling system can be digitalized by a system that consists of two major parts which are traffic rules violation detection and number plate recognition. In this research, these processes are done automatically which is based on machine learning, deep learning, and computer vision technology. Before starting this process, an object on the road is identified through the YOLOv3 algorithm. By using the OpenCV algorithm, traffic rules violation is detected and the vehicle that violated these rules is identified. To recognize the number plate of the vehicle, image acquisition, edge detection, segmentation of characters is done sequentially by using Convolution Neural Network (CNN) in MATLAB background. Among the traffic rules, the following traffic signal is implemented in this research.

Використання нейронної мережі для розроблення системи уникнення перешкод на дорозі

2020 ◽  
pp. 24-33
Author(s):  
Уляна Дзелендзяк ◽  
◽  
Мішель Вигриновський ◽  
Keyword(s):  
Neural Network ◽  
The Road ◽  
The Neural Network ◽  
On The Road

The possibility of using a neural network to implement a system of avoidance of obstacles on the road has been investigated. The algorithms based on which such a system can work has been reviewed, also the principle of learning of the neural network has been considered. In order to implement investigation the simulator based on Unity and ML Agents has been developed. Using simulator the efficiency of education and this neural network in different configurations has been investigated.

Study of Highway Crack Diagnosis Based on Cellular Neural Network

2013 ◽  
Vol 427-429 ◽  
pp. 2013-2017
Author(s):  
Sheng Zhuo Yao ◽  
Guo Dong Li ◽  
Fu Xin Zhang ◽  
Lin Ge
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Cellular Neural Network ◽  
Cellular Neural Networks ◽  
Quality Information ◽  
Image Detection ◽  
The Road ◽  
Standard Level ◽  
On The Road

Road quality information detect system is an important component in architecture quality detect system, also is the basement of successfully working of other related project for the whole country. The study of detecting the road crack is the key to insure the security of accurately detect the road quality in transportation system. In this paper, we come up with a fixed way of road undersized rift image detection by using cellular neural networks. By image processing, building rift networks and details networks and adding the model of similarity undersized rift networks. It can avoid the problem that can not accurately detect undersized crack by only taking the crack feature value. The experiment proved that fixed crack detect computing is easy to do, more accurate to detect the undersized cracks on the road and can reach the standard level of current detect technique.

Vehicle License Plate Registration Recognition System

2013 ◽  
Vol 718-720 ◽  
pp. 2286-2290
Author(s):  
Embiale Merkebu Tiruneh ◽  
De Ning Jiang
Keyword(s):  
Neural Network ◽  
Rapid Growth ◽  
Recognition System ◽  
License Plate ◽  
The Road ◽  
The Neural Network ◽  
Vehicle Registration ◽  
On The Road ◽  
Improved Technology ◽  
Analyze Data

Neural network had been used widely in many applications, such as to recognize an object or character, to detect a motion, to control a process, to forecast a result, to analyze data and for management of information. With the rapid growth of vehicles on the road and with the aid of improved technology, there is a demand for processing vehicles as conceptual resources in information systems. This paper will show how to design a system using the neural network to recognize the vehicle registration plate of vehicles. The approach to the project is by capturing footage and after which, the footage undergo segmentation to obtain the vehicle registration plate numbers using this software called MATLAB. The simulation will be illustrated after training the neural network; the system is able to recognize most of the vehicle registration plate with minimum errors.

scholarly journals The Prediction of Multistep Traffic Flow Based on AST-GCN-LSTM

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Fan Hou ◽  
Yue Zhang ◽  
Xinli Fu ◽  
Lele Jiao ◽  
Wen Zheng
Keyword(s):  
Neural Network ◽  
Traffic Flow ◽  
Road Network ◽  
Short Term Memory ◽  
The Road ◽  
Traffic Flow Prediction ◽  
Point Of Interest ◽  
Flow Prediction ◽  
Long Short Term Memory ◽  
On The Road

Aiming at the traffic flow prediction problem of the traffic network, this paper proposes a multistep traffic flow prediction model based on attention-based spatial-temporal-graph neural network-long short-term memory neural network (AST-GCN-LSTM). The model can capture the complex spatial dependence of road nodes on the road network and use LSGC (local spectrogram convolution) to capture spatial correlation features from the K-order local neighbors of the road segment nodes in the road network. It is more accurate to extract the information of neighbor nodes by replacing the single-hop neighborhood matrix with K-order local neighborhoods to expand the receptive field of graph convolution. The high-order neighborhood of road nodes is also fully considered instead of only extracting features from first-order neighbor nodes. In addition, an external attribute enhancement unit is designed to extract external factors (weather, point of interest, time, etc.) that affect traffic flow in order to improve the accuracy of the model’s traffic flow prediction. The experimental results show that when considering the static, dynamic, and static and dynamic combination, the model has excellent performance: RMSE (4.0406, 4.0362, 4.0234), MAE (2.7184, 2.7044, 2.7030), accuracy (0.7132, 0.7190, 0.7223).

scholarly journals Traffic Analysis Using Artificial Neural Network

2021 ◽  
pp. 359-365
Author(s):  
Mounica B ◽  
Nithya B S ◽  
Rakshitha N ◽  
Sirisha M
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Artificial Neural Network ◽  
Conventional Method ◽  
Image Sequences ◽  
Testing Model ◽  
Traffic Jam ◽  
The Road ◽  
On The Road

The vehicle traffic on the road is increasing progressively and managing such traffic on the roads are not stable by conventional method. To remove this traffic issue, we develop a project using machine learning in which we train the testing model as well as trained model of extracted traffic features. Extracted information from image sequences of testing model can give us real information to create the database which is the captured images like accident, foggy places, collision of the vehicles, traffic signal, no traffic jam, treefall etc. Choose any traffic image from the testing model, process and analyze the traffic image and the traffic image which was taken from the testing model is compared with the trained model of traffic images to determine the cause of the traffic. Image processing will be done to determine the cause of the traffic. This project is utilizing image processing methods designed to analyze and determine the cause of the traffic with the accuracy of the traffic caused. Thus, by using this project we can avoid the traffic and the time being wasted.

scholarly journals Forward-Lane Integrity Watchdog System

2019 ◽  
Vol 8 (4) ◽  
pp. 2784-2788
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Data Analysis ◽  
Collective Wisdom ◽  
Smart Vehicles ◽  
The Road ◽  
Neutral Networks ◽  
Highly Efficient ◽  
On The Road ◽  
New System

Today there exist a lot of smart vehicles which can change lane on their own, using their sensors to detect the vehicles around them and using various neural or non-neural algorithms to detect the lane on the road. But these are inherently limited to well-structured road environment and struggle with unstructured road or damaged road. This paper aims to propose a new system, based on cloud and deep-learning neutral networks to process images from each region to train a neural network to be highly efficient in that particular region. We use “Collective wisdom” of people along with data analysis to improve the accuracy of the model.

scholarly journals Recognizing atypical situations on the road with a convolutional neural network

2019 ◽  
Vol 38 (2) ◽  
pp. 38-44
Author(s):  
O.K. Kolesnytskiy ◽  
◽  
S.V. Kykynin ◽  
M.Yu. Derevyanko ◽  
A.A. Prepodobnyy Mendesh Da Maya ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
The Road ◽  
On The Road

scholarly journals Human-Computer Interaction with Special Emphasis on Converting Brain Signals to Speech

2020 ◽  
Vol 9 (6) ◽  
pp. 523-529
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Science Fiction ◽  
Linear Models ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
The Road ◽  
Brain Signals ◽  
On The Road ◽  
The Brain

Huge hurdle neuro engineers face on the road to effective brain-computer interfaces is attempting to translate the big selection of signals made by our brain into words pictures which may be simply communicable. The science-fiction plan of having the ability to manage devices or communicate with others simply by thinking is slowly but surely, obtaining nearer to reality. Translating brainwaves into words has been another large challenge for researchers, but again with the help of machine learning algorithms, superb advances are seen in recent years. The exploitation of deep learning and acceptable machine learning algorithms, the management signals from the brain will regenerate to some actions or some speech or text. For this, a neural network is created for the brain and conjointly a mapping is completed to catch all the brain signals in which neural network will be additionally used for changing these signals into actions. From the past literature, it is being concluded that the Deep Neural Networks are one of the main algorithms that are being placed into use for this research. This review article majorly focuses on studying the behavioral patterns generated by the brain signals and how they can be converted into actions effectively so that people suffering from semi or full paralysis can use this technology to live a normal life if not completely but to a certain extent. Also, it focuses on analyzing and drawing a comparison between linear and non-linear models and to conclude the best-suited model for the same currently available to the researchers.

scholarly journals Measuring the Road Traffic Intensity using Neural Network with Computer Vision

2018 ◽  
Vol 10 (1) ◽  
pp. 184
Author(s):  
Muhammad Hamdan ◽  
Othman Omran Khalifah ◽  
Teddy Surya Gunawan
Keyword(s):  
Neural Network ◽  
Computer Vision ◽  
Traffic Congestion ◽  
Road Traffic ◽  
Urban Traffic ◽  
Traffic Intensity ◽  
The Road ◽  
Surveillance Camera ◽  
On The Road ◽  
Alternative Routes

Traffic congestion plagues all driver around the world. To solve this problem computer vision can be used as a tool to develop alternative routes and eliminate traffic congestions. In the current generation with increasing number of cameras on the streets and lower cost for Internet of Things(IoT) this solution will have a greater impact on current systems. In this paper, the Macroscopic Urban Traffic model is used using computer vision as its source and traffic intensity monitoring system is implemented. The input of this program is extracted from a traffic surveillance camera and another program running a neural network classification which can classify and distinguish the vehicle type is on the road. The neural network toolbox is trained with positive and negative input to increase accuracy. The accuracy of the program is compared to other related works done and the trends of the traffic intensity from a road is also calculated.

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