Image Classification Using Machine Learning Techniques for Traffic Signal

This paper discusses the application of image classification that can be used in controlling a car, unmanned automatically, to pause, move, take deviation as and when any obstacles come on its way, also follow traffic signals. Our work will focus on making cars to avoid accidents, unlike the manual ones that are prone to accidents. The reasons may be many lapses on the part of the driver physically, mentally or sudden medical emergencies, etc. As part of this work, CNN classifiers have been used to perform image classification. Using CNN there was accuracy of 90%. Further, this accuracy may be increased to over 95%. This work involves the use of OpenCV that controls the hardware such as Raspberry Pi, four DC motors, Raspberry Pi cam, Arduino Uno.