Traffic Sign Detection for Motorists using Machine Learning

Traffic Sign Detection for Motorists using Machine Learning

Sejal Lawande, Yash Solanke, Akash Kawade, Riyaj Kazi

Department of Electronics and Telecommunication Engineering, Ajeenkya DY Patil School of Engineering, Lohegaon, Savitribai Phule Pune University, Pune, Maharashtra, India

Abstract - Motivated by the recent success of supervised and weakly supervised common object discovery, road signs are important to ensure smooth traffic flow without mishaps. Road symbols are pictorial representations having different necessary information required to be understood by the driver. Road signs in front of the vehicle are omitted by the drivers and this can lead to fatal accidents. This paper displays a summary of traffic sign board detection and recognition and implements a method to extract the road sign from a natural complicated image, process it, and warn the driver using voice command It is implemented in such a way that it acts as a boon to drivers to make efficient decisions Traffic sign detection is a critical component of advanced driver assistance systems (ADAS) and autonomous driving systems. This paper presents an approach for detecting traffic signs using machine learning techniques. The proposed method uses a convolutional neural network (CNN) to classify images of traffic signs.

Keywords: Traffic sign detection, CNN, Raspberry Pi, Camera Module.