| dc.description.abstract | The world is advancing technologically in all sectors, including intelligent transportation,
whereby various vehicles' movements are monitored and controlled remotely. These
technologies simplify the tasks in traffic control and increase road safety. The previous related
works implemented and designed provided different technologies that can identify, locate and
detect the vehicle's speed. Even though these technologies have been implemented, there is still
a lack of assistance to drivers for earlier knowing and reminding the road situation and to real-
time notify the dedicated authorities such as traffic police stations once an accident happens. In
this project, an Artificial Neural Network based smart number plate with real-time traffic sign
recognition and notification was developed. The developed smart number plate comprises two
parts, the smart plate and the display. The smart plate comprises a sensing and processing unit,
while the display comprises a notification unit, and both communicate through wireless
communication. The sensing unit contains a speed sensor, vibration shock sensors, a Global
Position System (GPS), and an AI-Thinker camera. The processing unit comprises espressif
board ESP32-CAM and ESP32-S that act as controllers. The notification unit contains the
Liquid crystal Display (LCD), Global System for Mobile communication (GSM), and Buzzer.
With the TensorFlow model for machine learning, the smart number plate classifies and
recognizes traffic speed signs with real-time notification. This smart number plate had been
tested on different vehicles, and it assisted drivers in obeying the traffic speed signs earlier, and
the traffic police station had been alerted for emergency support. Moreover, the remained traffic
signs such as informative traffic signs were not detected and was recommended to be added
into the future machine learning models. | en_US |
