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

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorBarbosa, Rodrigo Carvalho-
Autor(es): dc.creatorAyub, Muhammad Shoaib-
Autor(es): dc.creatorRosa, Renata Lopes-
Autor(es): dc.creatorZegarra Rodríguez, Demóstenes-
Autor(es): dc.creatorWuttisittikulkij, Lunchakorn-
Data de aceite: dc.date.accessioned2026-02-09T11:21:22Z-
Data de disponibilização: dc.date.available2026-02-09T11:21:22Z-
Data de envio: dc.date.issued2021-07-02-
Data de envio: dc.date.issued2021-07-02-
Data de envio: dc.date.issued2020-10-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/46643-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1139133-
Descrição: dc.descriptionMinimizing 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.-
Formato: dc.formatapplication/pdf-
Idioma: dc.languageen-
Publicador: dc.publisherMultidisciplinary Digital Publishing Institute - MDPI-
Direitos: dc.rightsacesso aberto-
Direitos: dc.rightshttp://creativecommons.org/licenses/by/4.0/-
Direitos: dc.rightshttp://creativecommons.org/licenses/by/4.0/-
???dc.source???: dc.sourceSensors Journal-
Palavras-chave: dc.subjectIntelligent traffic light-
Palavras-chave: dc.subjectDeep learning-
Palavras-chave: dc.subjectImage detection-
Palavras-chave: dc.subjectVehicle classification-
Palavras-chave: dc.subjectSemáforo inteligente-
Palavras-chave: dc.subjectAprendizagem profunda-
Palavras-chave: dc.subjectDetecção de imagem-
Palavras-chave: dc.subjectVeículos prioritários - Classificação-
Título: dc.titleLightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

Não existem arquivos associados a este item.