Traffic Sign Identification Using Modified LeNet-5 CNN

Abstract

Abstract: In 21st century, it is obvious that automobiles have become the standard mode of transportation. With the spread of their usage, road traffic becomes more intricate; hence road safety must be promising enough avoiding accidents. Drivers may sometimes overlook the sign boards along the roads which might lead to undesirable casualties. This could be helped by a system that could assist the driver in keeping an eye on the traffic sign boards on the way. It can be achieved using CNN i.e. Convolutional Neural Networks. CNNs are preferred when the applications are concerned with computer vision or image recognition. A deep learning CNN model is built in the LeNet-5 architecture with some modifications and is trained with traffic signs images from GTSRB dataset which stands for German traffic sign recognition benchmark with the help of OpenCV, Tensorflow, Keras and other libraries. The trained model is tested with test data and is observed to be recognizing any of the traffic signs which were already learnt by the network. Vehicle cameras come handy to capture the real-time sign boards. Implementation of this supervised learning model in real-time would aid road safety through vehicle control.