Autonomous Traffic Sign Detection for Self-Driving Car System Using Convolutional Neural Network Algorithm

The accurate detection of traffic signs is a critical component of self-driving systems, enabling safe and efficient navigation. In the literature, various methods have been investigated for traffic sign detection, among which deep learning-based approaches have demonstrated superior performance compared to other techniques. This paper justifies the widespread adoption of deep learning due to its ability to provide highly accurate results. However, the current research challenge lies in addressing the need for high accuracy rates and real-time processing requirements. In this study, we propose a convolutional neural network based on the YOLOv8 algorithm to overcome the aforementioned research challenge. The paper introduces an innovative solution in the form of a convolutional neural network based on the YOLOv8 architecture, underpinned by a custom dataset representing real-world traffic sign images and rigorous training. Through extensive experimentation, the proposed model not only proves highly effective but consistently achieves remarkable accuracy rates, successfully meeting the stringent real-time processing requirements crucial for self-driving systems, thus advancing the safety and efficiency of autonomous vehicles and shaping the future of transportation.