Real time traffic sign detection and recognition for autonomous vehicle

Abstract

The advancement of technology has made it possible for modern cars to utilize an increasing number of processing systems. Many methods have been developed recently to detect traffic signs using image processing algorithms. This study deals with an experiment to build a CNN model which can classify traffic signs in real-time effectively using OpenCV. The experimentation method involves building a CNN model based on a modified LeNet architecture with four convolutional layers, two max-pooling layers and two dense layers. The model is trained and tested with the German Traffic Sign Recognition Benchmark (GTSRB) dataset. Parameter tuning with different combinations of learning rate and epochs is done to improve the model’s performance. Later, this model is used to classify the images introduced to the camera in real- time. The graphs depicting the accuracy and loss of the model before and after parameter tuning are presented. Also, an experiment was performed to classify the traffic sign image introduced to the camera by using the CNN model. A high probability score is achieved during the process.