Solution of Real-Time Traffic Signs Detection Problem for Autonomous Vehicles by Using YOLOV4 and Haar Cascade Algorithms

Abstract

Unmanned systems are increasingly used today to facilitate our daily lives and use time more efficiently. Therefore, this rapidly emerging and growing technology appears in every aspect of our lives with its various functions. Object recognition algorithms are one of the most important functions that we often encounter in these systems. Autonomous vehicle technologies are the latest and fastest-growing technology among unmanned systems. In this study, we investigate the success rates of two different algorithms for recognizing traffic signs and markings that can be used for partially or fully autonomous vehicles. In this study, two different solutions to the problem of recognizing the signs for fully autonomous and semi-autonomous vehicles, respectively, were presented and the correct identification of the markers was evaluated. The work was performed in real-time. Two different concepts were used for these products. An enclosed space where an ideal lighting environment is provided for the evaluation of models should be visualized. In addition, for the general recognition of the models, the test procedures were performed with a dataset obtained from the users and it was computed for the general recognition. In addition, this study aims to provide a better understanding of the basic working principles, the differences between machine learning and deep learning, and the contents of object recognition processes. Within the scope of the study, the detection success of real-time traffic markers belonging to YOLOv4, and Haar Cascade algorithm was measured. In the study performed with the original dataset, the overall correct detection success rate because of the tests of the YOLOv4 algorithm was 99% on average, and the correct detection success rate obtained because of the testing of the Haar Cascade algorithm was 61%.