Performance Comparison in Traffic Sign Recognition Using Deep Learning

In recent years, along with the increase in private cars, traffic signs have increased in quantity, demanding greater attentiveness from drivers. Many studies have been conducted on Traffic Sign Recognition (TSR) to enhance road safety and driver assistance systems by enabling vehicles to autonomously detect and interpret traffic signs, providing crucial information to drivers in real-time. This paper examines various traffic sign detection and classification models, which give practitioners and researchers valuable insights into selecting optimal solutions tailored to the needs of real-world applications. Notably, the investigation highlights YOLOv8 as a leading detection model, displaying exceptional results with an mAP of 99.4%. YOLOv8 provides various model sizes allowing for adaptation to specific real-time processing scenarios. On the other hand, the LeNet model is a standout performer in the classification domain, consistently achieving a remarkable accuracy of 98.2% while using only 0.4 million parameters. The LeNet architecture ensures accurate and rapid traffic sign classification, making it an appealing choice for applications where resource efficiency is critical.