scholarly journals The Road Traffic Sign Recognition and Automatic Positioning for Road Facility Management

2013 ◽  
Vol 15 (1) ◽  
pp. 155-161 ◽  
Author(s):  
Jun Seok Lee ◽  
Duk Geun Yun
Keyword(s):  
Road Traffic ◽  
Facility Management ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
The Road ◽  
Sign Recognition ◽  
Automatic Positioning

scholarly journals Transfer Learning Based Traffic Sign Recognition System for Surveillance Application

2019 ◽  
Vol 8 (3) ◽  
pp. 573-576
Keyword(s):  
Transfer Learning ◽  
Road Traffic ◽  
Recognition System ◽  
Vehicle Tracking ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Wide Range ◽  
Traffic Recognition ◽  
Surveillance Application

Road Traffic Recognition is very important in many applications, such as automated deployment, traffic mapping, and vehicle tracking. Proposed traffic sign recognition system tails the transfer learning method that is frequently used in neural network uses. The benefit of expending this technique is that the initially network has been trained with a rich set of features appropriate to a wide range of images. Once the network is trained , learning can be transferred to the new activity adjustment to the network. Firsthand Indian traffic sign dataset is used.New results exhibit that the suggested method can accomplish modest outcomes when matched with other related techniques.

scholarly journals Road Traffic Sign Recognition and Vehicle Accident Avoidance System

2020 ◽  
pp. 484-489
Author(s):  
Snehal Lahare ◽  
Ankit Mishra ◽  
Ashish Nair ◽  
Nutan Borkar
Keyword(s):  
Road Traffic ◽  
Raspberry Pi ◽  
Traffic Sign Recognition ◽  
Driver Assistance Systems ◽  
Traffic Sign ◽  
Real Time System ◽  
Automatic Driving ◽  
Vehicle Accident ◽  
Sign Recognition ◽  
System Output

Traffic sign recognition and vehicle accident avoidance system gets a of interest late by huge scale organizations, e.g., Apple, Google and Volkswagen and so on driven by the market requirements for smart applications, e.g. Automatic Driving and Driver Assistance Systems , Mobile Eye, Mobile Mapping and many more.In this paper, traffic sign recognition and vehicle accident avoidance system is utilized to keep up traffic and maintain a strategic distance from vehicle, caution the occupied drivers, and avoid activities that can lead a vehicle. An on-going programmed sign recognition and detection can support the driver with safety. System propose automated real time system which will capture the traffic sign and show it at driver dashboard with front obstacle exact distance on screen. The PiCam is associated with Raspberry Pi and it is utilized to capture pictures .Screen is utilized to show the system output e.g. appearing of traffic sign and separation of vehicle. This framework is configuration to maintain a strategic distance from vehicle happening on street.

scholarly journals Improved VGG Model for Road Traffic Sign Recognition

2018 ◽  
Vol 57 (1) ◽  
pp. 11-24 ◽  
Author(s):  
Shuren Zhou ◽  
Wenlong Liang ◽  
Junguo Li ◽  
Jeong-Uk Kim
Keyword(s):  
Road Traffic ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition

scholarly journals Efficient Traffic Sign Recognition Using CLAHE-Based Image Enhancement and ResNet CNN Architectures

2021 ◽  
Vol 15 (4) ◽  
pp. 0-0
Keyword(s):  
Image Enhancement ◽  
Autonomous Driving ◽  
Histogram Equalization ◽  
Experimental Results ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
The Road ◽  
Sign Recognition ◽  
On The Road

Recognition and classification of traffic signs and other numerous displays on the road are very crucial for autonomous driving, navigation, and safety systems on roads. Machine learning or deep learning methods are generally employed to develop a traffic sign recognition (TSR) system. This paper proposes a novel two-step TSR approach consisting of contrast limited adaptive histogram equalization (CLAHE)-based image enhancement and convolutional neural network (CNN) as multiclass classifier. Three CNN architectures viz. LeNet, VggNet, and ResNet were employed for classification. All the methods were tested for classification of German traffic sign recognition benchmark (GTSRB) dataset. The experimental results presented in the paper endorse the capability of the proposed work. Based on experimental results, it has also been illustrated that the proposed novel architecture consisting of CLAHE-based image enhancement & ResNet-based classifier has helped to obtain better classification accuracy as compared to other similar approaches.

scholarly journals Traffic Sign Recognition based on Synthesised Training Data

2018 ◽  
Vol 2 (3) ◽  
pp. 19
Author(s):  
Alexandros Stergiou ◽  
Grigorios Kalliatakis ◽  
Christos Chrysoulas
Keyword(s):  
Real World ◽  
Road Traffic ◽  
Recognition System ◽  
Training Data ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Real World Data ◽  
Visual Appearance ◽  
Sign Recognition ◽  
High Recognition Accuracy

To deal with the richness in visual appearance variation found in real-world data, we propose to synthesise training data capturing these differences for traffic sign recognition. The use of synthetic training data, created from road traffic sign templates, allows overcoming the problems of existing traffic sing recognition databases, which are only subject to specific sets of road signs found explicitly in countries or regions. This approach is used for generating a database of synthesised images depicting traffic signs under different view-light conditions and rotations, in order to simulate the complexity of real-world scenarios. With our synthesised data and a robust end-to-end Convolutional Neural Network (CNN), we propose a data-driven, traffic sign recognition system that can achieve not only high recognition accuracy, but also high computational efficiency in both training and recognition processes.

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