Traffic light and sign detection for autonomous land vehicle using Raspberry Pi

This work aims to implement traffic light and sign detection using Image processing technique for an autonomous and vehicle. Traffic Sign Recognition system is used to regulate traffic signs, warn a driver and command certain actions. Fast robust and real-time automatic traffic sign detection and recognition can support the driver and significantly increase driving safety. Automatic recognition of traffic signs is also important for an automated intelligent driving vehicle or for a driver assistance system. This is a visual based project i.e., the input to the system is video data which is continuously captured from the webcam is interfaced to the Raspberry Pi. Images are pre-processed with several image processing techniques such as; Hue, Saturation and Value (HSV) color space model technique is employed for traffic light detection, for sign detection again HSV color space model and Contour Algorithm has been used. The signs are detected based on Region of Interest (ROI). The ROI is detected based on the features like geometric shape and color of the object in the image containing the traffic signs. The experimental results show highly accurate classifications of traffic sign patterns with complex background images as well as the results accomplish in reducing the computational cost of this proposed method.