| Full text | |
| Author(s): |
Barbosa, Rodrigo Carvalho
[1]
;
Ayub, Muhammad Shoaib
[2]
;
Rosa, Renata Lopes
[1]
;
Rodriguez, Demostenes Zegarra
[1]
;
Wuttisittikulkij, Lunchakorn
[2]
Total Authors: 5
|
| Affiliation: | [1] Univ Fed Lavras, Dept Comp Sci, BR-37200000 Lavras, MG - Brazil
[2] Chulalongkorn Univ, Dept Elect Engn, Bangkok 10330 - Thailand
Total Affiliations: 2
|
| Document type: | Journal article |
| Source: | SENSORS; v. 20, n. 21 NOV 2020. |
| Web of Science Citations: | 0 |
| 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. (AU) | |
| FAPESP's process: | 19/07665-4 - Center for Artificial Intelligence |
| Grantee: | Fabio Gagliardi Cozman |
| Support Opportunities: | Research Grants - Research Program in eScience and Data Science - Research Centers in Engineering Program |
| FAPESP's process: | 18/26455-8 - Audio-Visual Speech Processing by Machine Learning |
| Grantee: | Miguel Arjona Ramírez |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 18/12579-7 - ELIOT: enabling technologies for IoT |
| Grantee: | Vitor Heloiz Nascimento |
| Support Opportunities: | Research Projects - Thematic Grants |