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 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 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.

scholarly journals Implementation paper of Traffic Signal Detection and Recognition using deep learning

2021 ◽  
Vol 12 (1S) ◽  
pp. 212-219
Author(s):  
Nikhil S. Rajguru, Et. al.
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Large Scale ◽  
Autonomous Driving ◽  
Traffic Signals ◽  
Raspberry Pi ◽  
Driver Assistance Systems ◽  
Traffic Sign ◽  
Efficient Manner ◽  
Detection And Recognition

Traffic boards and traffic signals are used to maintain proper traffic through busy roads. They help to recognize the rules to follow when driving the vehicle. These signs warn the distracted driver, and prevent his/her actions which could lead to an accident. We have proposed a system which can help recognize these boards and signals at real time thus avoiding major mishap. A real-time automatic sign detection and recognition can help the driver, significantly increasing his/her safety. Lately traffic sign recognition has got an immense interest lately by large scale companies such as Google, Apple and Volkswagen etc. which is driven by the market needs for intelligent applications such as autonomous driving, driver assistance systems (ADAS), mobile mapping, Mobil eye, Apple, etc.  Hence, here, we have implemented to do the same with cost efficient manner using Raspberry Pi. The proposed system detects the traffic board or traffic signals, capture its image which through deep learning approach recognizes the same to give result on dashboard as well it gives the measures of distance from front obstacle which helps to implement brake system if obstacle is near. PiCam is used to capture images of traffic sings and is connected to RaspberryPi. Monitor is used to display required output, showing type of sign and distance of collision. This proposal will avoid large number of accidents occurring at bridges and work in progress area due to automated braking system and simultaneous reduce death ratio.

scholarly journals Logistic Regression Based Model for Improving the Accuracy and Time Complexity of ROI’s Extraction in Real Time Traffic Signs Recognition System

2019 ◽  
Vol 1 (1) ◽  
Author(s):  
Fareed Qararyah ◽  
Yousef-Awwad Daraghmi ◽  
Eman Yasser Daraghmi
Keyword(s):  
Logistic Regression ◽  
Real Time ◽  
Color Space ◽  
Computation Time ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Traffic Signs ◽  
Real Time Traffic ◽  
Sign Recognition ◽  
Recognition Systems

Designing accurate and time-efficient real-time traffic sign recognition systems is a crucial part of developing the intelligent vehicle which is the main agent in the intelligent transportation system. Traffic sign recognition systems consist of an initial detection phase where images and colors are segmented and fed to the recognition phase. The most challenging process in such systems in terms of time consumption is the detection phase. The tradeoff in previous studies, which proposed different methods for detecting traffic signs, is between accuracy and computation time. Therefore, this paper presents a novel accurate and time-efficient color segmentation approach based on logistic regression. We used RGB color space as the domain to extract the features of our hypothesis; this has boosted the speed of our approach since no color conversion is needed. Our trained segmentation classifier was tested on 1000 traffic sign images taken in different lighting conditions. The results show that our approach segmented 974 of these images correctly and in a time less than one-fifth of the time needed by any other robust segmentation method.

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