scholarly journals Real-time traffic sign detection and recognition using Raspberry Pi

2022 ◽  
Vol 12 (1) ◽  
pp. 331
Author(s):  
Ida Syafiza Binti Md Isa ◽  
Choy Ja Yeong ◽  
Nur Latif Azyze bin Mohd Shaari Azyze
Keyword(s):  
Real Time ◽  
Video Recording ◽  
Recognition System ◽  
Road Accident ◽  
Raspberry Pi ◽  
Traffic Sign ◽  
Traffic Signs ◽  
The Road ◽  
Real Time Traffic ◽  
Driving Assistance

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.

scholarly journals Real Time Traffic Signs and Obstacle Detection in Self-Driving Car

2020 ◽  
Vol 9 (6) ◽  
pp. 253-256
Keyword(s):  
Real Time ◽  
Research Work ◽  
Traffic Signals ◽  
Obstacle Detection ◽  
Raspberry Pi ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Detection Model ◽  
Emergency Braking ◽  
Real Time Traffic

The motivation behind this research work is to improve car safety and efficiency.The concept of self driving cars is heard from years, it has not come into usage in many countries because of the lack of complete intelligence in the vehicle. Some of the modern vehicles provide partially automated specifications such as keeping the car within its lane, speed controls or emergency braking..According to statistics most of the accidents occur due to lack of instant response to traffic signs and obstacles ahead. In case of self driving car this problem can be addressed by detecting the traffic signals using high end camera. Real time traffic sign detection model accomplishes its objective by identifying the traffic signals and obstacles. A high end camera is used to capture the image, raspberry pi 3 is used as hardware and open computer vision library is used to process the image and identify the patterns in the image to properly detect the signals. Ultra sonic distance sensor is used to identify the obstacles.

scholarly journals Real-Time Traffic Sign Detection and Recognition Method Based on Simplified Gabor Wavelets and CNNs

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3192 ◽  
Author(s):  
Faming Shao ◽  
Xinqing Wang ◽  
Fanjie Meng ◽  
Ting Rui ◽  
Dong Wang ◽  
...  
Keyword(s):  
Real Time ◽  
Gabor Wavelets ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Automatic Driving ◽  
Real Time Traffic ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition ◽  
Grayscale Images

Traffic sign detection and recognition plays an important role in expert systems, such as traffic assistance driving systems and automatic driving systems. It instantly assists drivers or automatic driving systems in detecting and recognizing traffic signs effectively. In this paper, a novel approach for real-time traffic sign detection and recognition in a real traffic situation was proposed. First, the images of the road scene were converted to grayscale images, and then we filtered the grayscale images with simplified Gabor wavelets (SGW), where the parameters were optimized. The edges of the traffic signs were strengthened, which was helpful for the next stage of the process. Second, we extracted the region of interest using the maximally stable extremal regions algorithm and classified the superclass of traffic signs using the support vector machine (SVM). Finally, we used convolution neural networks with input by simplified Gabor feature maps, where the parameters were the same as the detection stage, to classify the traffic signs into their subclasses. The experimental results based on Chinese and German traffic sign databases showed that the proposed method obtained a comparable performance with the state-of-the-art method, and furthermore, the processing efficiency of the whole process of detection and classification was improved and met the real-time processing demands.

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