Please use this identifier to cite or link to this item: http://repositorio.ufla.br/jspui/handle/1/46643
Title: Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights
Keywords: Intelligent traffic light
Deep learning
Image detection
Vehicle classification
Semáforo inteligente
Aprendizagem profunda
Detecção de imagem
Veículos prioritários - Classificação
Issue Date: Oct-2020
Publisher: Multidisciplinary Digital Publishing Institute - MDPI
Citation: BARBOSA, R. C. et al. Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights. Sensors, [S. I.], v. 20, n. 21, 2020. DOI: 10.3390/s20216218.
Abstract: 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.
URI: http://repositorio.ufla.br/jspui/handle/1/46643
Appears in Collections:DCC - Artigos publicados em periódicos



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