Published Paper Details:

Stop signs are the primary form of traffic control in the Country . However, they have a tendency to be much less effective than other forms of traffic control like traffic lights. This is due to their smaller size, lack of lighting, and the fact that they may become visually obscured from the road. In this paper, we offer a solution to this problem in the form of a detector. It is designed to alert a driver when they are approaching a stop sign using a voice notification system (VNS). A field test was performed in a snowy environment. The test results demonstrate that the application can detect all of the stop signs correctly, even when some of them were obstructed by the snow, which in turn greatly improves the user awareness of stop signs. Acknowledgement of traffic signs vary significantly in numerous applications, for example, in self-driving vehicle/driverless vehicle, traffic planning and traffic observation. Traffic Sign Recognition (TSR) framework is a segment of Driving Assistance System (ADAS). This research work has developed a YOLOV3 model to identify the stop signs present in the image given by user. The darknet algorithm is used in the YOLOV3 model, which has a pre- trained dataset. The framework helps drivers in safe driving by providing significant data from street signs. The automobile industry has grown significantly, and some companies are aiming to build self-contained automobiles, with stop sign recognition being one of the most important elements to consider.