A Study on YOLOv11-Based Traffic Monitoring Systems
Yashika Chauhan, Neha Kaushish, Brijesh Kumar · 2026
This paper explores the use of a state-of-the-art object-detection model, YOLOv11, in traffic monitoring networks to deal with these issues. Using the architecture of the YOLOv11 object detection model to detect vehicles and pedestrians, the study analyses its functionality under different conditions, such as changing lighting, weather, and traffic conditions, on pre-existing datasets and simulation software. The results indicate that YOLOv11 is a powerful tool in terms of real-time performance, and its detection capabilities provide a high level of situational awareness and shorten the time-to- response time in the event of traffic accidents. It proves to be flexible in complex urban environments, and it is better than its predecessors at managing occlusions and multi-object situations. To sum up, YOLOv11-based systems are a potential breakthrough in the field of intelligent transportation infrastructure, as they allow for either reacting proactively or enhancing road safety. This highlights the possibility of incorporating state-of-the-art AI models to promote the sustainable urban movement.