Scalable and Efficient Traffic Sign Recognition Leveraging Yolov11
Jeswin K Johnson, E. Bijolin Edwin, V. Ebenezer, Stewart Kirubakaran S, M. Roshni Thanka · 2025
In order to preserve road safety and efficient traffic control, traffic sign detection is a crucial component of autonomous vehicles and intelligent transportation systems. Through the use of YOLOv11, an enhanced YOLO algorithm that is tailored to maintain high precision and instantaneous performance, we propose a dependable method for recognizing and classifying traffic signs. The model was trained and validated using the Indian Traffic Sign dataset. Additional preprocessing approaches were used to enhance detection in difficult situations such complicated backdrops, barriers, and varying lighting. Experimental results demonstrate significant improvements in processing efficiency and detection accuracy compared to existing approaches. In addition to providing possible uses for advanced driving assistance systems (ADAS), this work advances the state of traffic sign recognition systems.