Improved Multi-Type Vehicle Recognition with a Customized YOLO

Introducing a novel strategy designed to enhance urban mobility through intelligent traffic management, this research employs a customized YOLO (You Only Look Once) object detection system. The system seamlessly integrates cutting-edge computer vision methodologies with real-time data processing to precisely detect and categorize various entities such as vehicles, pedestrians, and other objects within urban settings. The objective of this research work is to achieve notable advancements in accuracy, particularly concerning traffic surveillance and regulation, with a measured accuracy of 89.07%. However, challenges such as scalability, computational complexity, and data robustness need to be addressed for successful deployment and widespread adoption of these systems. The outcomes of this study showcase substantial improvements in traffic flow dynamics, evident through reduced congestion levels and bolstered safety measures, thereby underscoring the immense potential of intelligent systems in optimizing urban mobility challenges.