| Texto completo | |
| Autor(es): |
Barbosa, Rodrigo Carvalho
[1]
;
Ayub, Muhammad Shoaib
[2]
;
Rosa, Renata Lopes
[1]
;
Rodriguez, Demostenes Zegarra
[1]
;
Wuttisittikulkij, Lunchakorn
[2]
Número total de Autores: 5
|
| Afiliação do(s) autor(es): | [1] Univ Fed Lavras, Dept Comp Sci, BR-37200000 Lavras, MG - Brazil
[2] Chulalongkorn Univ, Dept Elect Engn, Bangkok 10330 - Thailand
Número total de Afiliações: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | SENSORS; v. 20, n. 21 NOV 2020. |
| Citações Web of Science: | 0 |
| Resumo | |
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. (AU) | |
| Processo FAPESP: | 19/07665-4 - Centro de Inteligência Artificial |
| Beneficiário: | Fabio Gagliardi Cozman |
| Modalidade de apoio: | Auxílio à Pesquisa - Programa eScience e Data Science - Centros de Pesquisa Aplicada |
| Processo FAPESP: | 18/26455-8 - Processamento Audiovisual de Voz por Aprendizagem de Máquina |
| Beneficiário: | Miguel Arjona Ramírez |
| Modalidade de apoio: | Auxílio à Pesquisa - Regular |
| Processo FAPESP: | 18/12579-7 - Tecnologias habilitadores para a Internet das Coisas |
| Beneficiário: | Vitor Heloiz Nascimento |
| Modalidade de apoio: | Auxílio à Pesquisa - Temático |