Traffic Signs Detection and Recognition System using Deep Learning
Abstract
With the rapid development of technology, automobiles have become an essential asset in our day-to-day lives. One of the more important researches is Traffic Signs Recognition (TSR) systems. This paper describes an approach for efficiently detecting and recognizing traffic signs in real-time, taking into account the various weather, illumination and visibility challenges through the means of transfer learning. We tackle the traffic sign detection problem using the state-of-the-art of multi-object detection systems such as Faster Recurrent Convolutional Neural Networks (F-RCNN) and Single Shot Multi- Box Detector (SSD) combined with various feature extractors such as MobileNet v1 and Inception v2, and also Tiny-YOLOv2. However, the focus of this paper is going to be F-RCNN Inception v2 and Tiny YOLO v2 as they achieved the best results. The aforementioned models were fine-tuned on the German Traffic Signs Detection Benchmark (GTSDB) dataset. These models were tested on the host PC as well as Raspberry Pi 3 Model B+ and the TASS PreScan simulation. We will discuss the results of all the models in the conclusion section.
Cite
@article{arxiv.2003.03256,
title = {Traffic Signs Detection and Recognition System using Deep Learning},
author = {Pavly Salah Zaki and Marco Magdy William and Bolis Karam Soliman and Kerolos Gamal Alexsan and Keroles Khalil and Magdy El-Moursy},
journal= {arXiv preprint arXiv:2003.03256},
year = {2020}
}
Comments
7 pages, 14 figures, 10 tables