Utilizing Image Processing and the YOLOv3 Network for Real-Time Traffic Light Control

S. Francisco Segura Altamirano, Diana M. Castro Cárdenas, Ayax M. Sifuentes Montes, Lucia I. Chaman Cabrera, Esther Y. Lizana Puelles, Angel M. Rojas Coronel, Oscar M. De La Cruz Rodríguez, Luis A. Lara Romero

Research output: Contribution to journalArticlepeer-review


In this study, different strategies used to count vehicles and people in different image areas at a street intersection were analyzed to obtain counts at appropriate times suitable for real-time control of a traffic light. To achieve this, video recordings of cameras placed at the intersection were used to test and verify image processing algorithms and deep learning using the YOLOv3 network implemented on a 4 GB RAM Jetson Nano card. We counted the vehicles and people that stopped and crossed the polygons to delimit the different areas of interest, with a maximum error of ±2 in the validation tests for all cases. In addition, as a strategy, we combined the images from both cameras into a single one, thereby allowing us to make a single detection and subsequently determine if they are inside or outside the polygons used in separating the areas of interest with the respective counts. Furthermore, this enabled us to obtain information on vehicles and people stopped and crossing in a time of 0.73 s on average. Hence, it was established that the inclusion of the control algorithm is appropriate for real-time control of traffic lights.

Original languageEnglish
Article number4547821
JournalJournal of Engineering (United Kingdom)
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2023 S. Francisco Segura Altamirano et al.


Dive into the research topics of 'Utilizing Image Processing and the YOLOv3 Network for Real-Time Traffic Light Control'. Together they form a unique fingerprint.

Cite this