Abstract:
Detecting traffic signs and lights has been a problem for intelligent vehicles for a long time.Prior
to classifying traffic signs and lights, this provides an efficient method for managing the inventory
of traffic signs and lights for driver assistance or autonomous vehicles. Object identification mod-
els like Fast RCNN and Faster RCNN have been applied to this issue. These approaches’ main
drawbacks are their slowness and inability to sort in real time. Deep learning was used to create
the Convolutional Neural Network (CNN) for visual object detection. This has helped to fix the
problems with traditional object recognition. In this proposed plan, CNN and YOLO architecture
have been chosen as ways to find and sort things. Here, the newest version of the YOLOV5 was
used. The YOLOV5 is mostly used because it is fast, has a simple design, and works well for most
object detection tasks. In this system, a method for detecting and identifying traffic signs and lights
is proposed. This method, along with deep CNN, is used to classify traffic signs and lights. The
proposed system works in real time to find and identify images of traffic lights and signs.
