Traffic Signs Detection and Recognition System using Deep Learning
This work addresses traffic sign recognition for autonomous driving systems, but it is incremental as it applies existing methods to a known dataset with standard fine-tuning.
The paper tackled traffic sign detection and recognition in real-time under varying weather and illumination conditions by fine-tuning deep learning models like F-RCNN Inception v2 and Tiny YOLO v2 on the GTSDB dataset, achieving best results with these models tested on PC, Raspberry Pi, and simulation environments.
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.