Abstract:
Traffic signs are important in communicating
information to drivers. Thus, comprehension of traffic signs is
essential for road safety and ignorance may result in road
accidents. Traffic sign detection has been a research spotlight
over the past few decades. Real-time and accurate detections are
the preliminaries of robust traffic sign detection system which is
yet to be achieved. This study presents a voice-assisted real-time
traffic sign recognition system which is capable of assisting
drivers. This system functions under two subsystems. Initially,
the detection and recognition of the traffic signs are carried out
using a trained Convolutional Neural Network (CNN). After
recognizing the specific traffic sign, it is narrated to the driver
as a voice message using a text-to-speech engine. An efficient
CNN model for a benchmark dataset is developed for real-time
detection and recognition using Deep Learning techniques. The
advantage of this system is that even if the driver misses a traffic
sign, or does not look at the traffic sign, or is unable to
comprehend the sign, the system detects it and narrates it to the
driver. A system of this type is also important in the
development of autonomous vehicles.
