- Tytuł:
- AI-based Yolo v4 intelligent traffic light control system
- Autorzy:
-
Prathap, Boppuru Rudra
Kumar, Kukatlapalli Pradeep
Chowdary, Cherukuri Ravindranath
Hussain, Javid - Powiązania:
- https://bibliotekanauki.pl/articles/27314354.pdf
- Data publikacji:
- 2022
- Wydawca:
- Sieć Badawcza Łukasiewicz - Przemysłowy Instytut Automatyki i Pomiarów
- Tematy:
-
traffic jam
traffic light system
traffic management
intelligent monitoring
signal switching algorithm
artificial intelligence - Opis:
- With the growing number of city vehicles, traffic management is becoming a persistent challenge. Traffic bottlenecks cause significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, wait times for commuters at traffic signal points are not reduced. The proposed methodology employs artificial intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in shorter vehicle waiting times.
- Źródło:
-
Journal of Automation Mobile Robotics and Intelligent Systems; 2022, 16, 4; 53--61
1897-8649
2080-2145 - Pojawia się w:
- Journal of Automation Mobile Robotics and Intelligent Systems
- Dostawca treści:
- Biblioteka Nauki
Artykuł