Automatic Warning System for Drivers using Deep Learning Algorithm

The problem related to traffic is a complex one requiring proper design and planning for developing an efficient solution. Drivers are expected to pay attention to identify traffic signs, interpret and follow them while driving. Many of the accidents occur because drivers lose their attention and ignore the street signs. An automatic system in a car which detects, recognizes, interprets and gives warning to the driver would be a great help in reducing the number of road accidents. This paper proposes an embedded system based on Raspberry Pi 3 and uses CNN (Convolution Neural Network) algorithm to detect and identify the captured traffic signs and activate the motor accordingly. Among the different activation functions ReLu is better but its derivative is zero fo r negative values. Hence in this paper a new activation function is proposed which gives a better accuracy, less loss and execution time when compared with ReLu. The detection of traffic sign boards will send the output signal to PIC (Peripheral Interface Controllers) microcontroller which will compare the input value from Raspberry Pi with the rotating speed of DC motor. According to the control signal from PIC microcontroller, servomotor turns 45 deg for controlling ABS system and the speed of the vehicle is thus reduced when a sign board is detected. LCD displays the speed in which the vehicle is running. The CNN model with proposed activation function gives an accuracy of 93% with sensitivity and specificity above 70% and 55% respectively.