HybridNet: A fast vehicle detection system for autonomous driving

2019 ◽  
Vol 70 ◽  
pp. 79-88 ◽  
Author(s):  
Xuerui Dai
Keyword(s):  
Detection System ◽  
Vehicle Detection ◽  
Autonomous Driving

Acoustic Signal Based Automatic Vehicle Detection System

2012 ◽  
Vol 2 (4) ◽  
pp. 88-89
Author(s):  
Sai Sandeep.k Sai Sandeep.k ◽  
◽  
P. Vijay Kumar
Keyword(s):  
Acoustic Signal ◽  
Detection System ◽  
Vehicle Detection

A vehicle detection system based on Haar and Triangle features

2009 ◽  
Author(s):  
Anselm Haselhoff ◽  
Anton Kummert
Keyword(s):  
Detection System ◽  
Vehicle Detection

Real-time Pedestrian and Vehicle Detection for Autonomous Driving

2018 ◽  
Author(s):  
Zhiheng Yang ◽  
Jun Li ◽  
Huiyun Li
Keyword(s):  
Real Time ◽  
Vehicle Detection ◽  
Autonomous Driving

scholarly journals Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6218
Author(s):  
Rodrigo Carvalho Barbosa ◽  
Muhammad Shoaib Ayub ◽  
Renata Lopes Rosa ◽  
Demóstenes Zegarra Rodríguez ◽  
Lunchakorn Wuttisittikulkij
Keyword(s):  
Deep Learning ◽  
Traffic Control ◽  
Detection System ◽  
Lightweight Design ◽  
Vehicle Detection ◽  
Activation Function ◽  
Urban Traffic ◽  
Vehicle Classification ◽  
Traffic Light ◽  
Traffic Lights

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

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