An Improved Traffic Sign Recognition and Road Lane Detection for Self-Driving Cars using YOLO-v8

Abstract

This paper presents a unique way for improving the efficiency and efficacy of self-driving cars through enhanced traffic sign recognition and road lane detection using the deep learning framework. The selected solution demonstrates good performance across a wide range of environmental challenges by thoroughly training the YOLO model on a massive dataset including various lighting factors, weather scenarios, and other aspects. The model's effectiveness at recognising and categorising multiple traffic signs in a range of circumstances, including low-light and severe weather, has been enhanced. Considering YOLOv8's versatility, the model is trained on a wide range of road lane video datasets, ensuring exact vehicle localization between lanes.Deep convolutional neural network (DCNN) based on residual network 50 (resnet) architecture for sign and lane identification, as well as you only look once (YOLOv8), an advanced CNN technique for real-time object detection, were used to accomplish the proposed model.