1 research outputs found
Traffic Signs Detection and Recognition System using Deep Learning
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.Comment: 7 pages, 14 figures, 10 table