Traffic Signs Detection and Recognition System using Deep Learning

• URL: http://arxiv.org/abs/2003.03256v1
• Date: Fri, 6 Mar 2020 14:54:40 GMT
• Title: Traffic Signs Detection and Recognition System using Deep Learning
• Authors: Pavly Salah Zaki, Marco Magdy William, Bolis Karam Soliman, Kerolos Gamal Alexsan, Keroles Khalil, and Magdy El-Moursy
• Abstract summary: This paper describes an approach for efficiently detecting and recognizing traffic signs in real-time. We tackle the traffic sign detection problem using the state-of-the-art of multi-object detection systems. The focus of this paper is going to be F-RCNN Inception v2 and Tiny YOLO v2 as they achieved the best results.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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