Real-time traffic sign detection and recognition using Raspberry Pi

Abstract

Nowadays, the number of road accident in Malaysia is increasing expeditiously. One of the ways to reduce the number of road accident is through the development of the advanced driving assistance system (ADAS) by professional engineers. Several ADAS system has been proposed by taking into consideration the delay tolerance and the accuracy of the system itself. In this work, a traffic sign recognition system has been developed to increase the safety of the road users by installing the system inside the car for driver’s awareness. TensorFlow algorithm has been considered in this work for object recognition through machine learning due to its high accuracy. The algorithm is embedded in the Raspberry Pi 3 for processing and analysis to detect the traffic sign from the real-time video recording from Raspberry Pi camera NoIR. This work aims to study the accuracy, delay and reliability of the developed system using a Raspberry Pi 3 processor considering several scenarios related to the state of the environment and the condition of the traffic signs. A real-time testbed implementation has been conducted considering twenty different traffic signs and the results show that the system has more than 90% accuracy and is reliable with an acceptable delay.

Identification of traffic signs for advanced driving assistance systems in smart cities using deep learning

“…CNN has shown excellent results earlier [ 15 ] in traffic sign classification and is recently used in traffic sign detection. Yihui Wu et al applied deep learning to reject non-traffic sign [ 10 ]. A fully convolutional network and deep CNN for classification are performed and extended to recognize traffic signs [ 1 ].…”

An intelligent road accident reduction system using device-to-device communication

“…By this approach, the rate of accidents each year can be reduced up to 80%. A real-time implementation of a testbed has been introduced that got 90% accuracy followed by an acceptable delay to recognize traffic systems for preventing road accidents using tensorflow and Raspberry Pi [18]. The existing problems and drawbacks of VANETs have been discussed in [19].…”