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.

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 The Implications of Weather and Reflectivity Variations on Automatic Traffic Sign Recognition Performance

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Mudasser Seraj ◽  
Andres Rosales-Castellanos ◽  
Amr Shalkamy ◽  
Karim El-Basyouny ◽  
Tony Z. Qiu
Keyword(s):  
Object Detection ◽  
Real World ◽  
System Analysis ◽  
Recognition Performance ◽  
Weather Conditions ◽  
Recognition System ◽  
Warning Signs ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition

Automatic recognition of traffic signs in complex, real-world environments has become a pressing research concern with rapid improvements of smart technologies. Hence, this study leveraged an industry-grade object detection and classification algorithm (You-Only-Look-Once, YOLO) to develop an automatic traffic sign recognition system that can identify widely used regulatory and warning signs in diverse driving conditions. Sign recognition performance was assessed in terms of weather and reflectivity to identify the limitations of the developed system in real-world conditions. Furthermore, we produced several editions of our sign recognition system by gradually increasing the number of training images in order to account for the significance of training resources in recognition performance. Analysis considering variable weather conditions, including fair (clear and sunny) and inclement (cloudy and snowy), demonstrated a lower susceptibility of sign recognition in the highly trained system. Analysis considering variable reflectivity conditions, including sheeting type, lighting conditions, and sign age, showed that older engineering-grade sheeting signs were more likely to go unnoticed by the developed system at night. In summary, this study incorporated automatic object detection technology to develop a novel sign recognition system to determine its real-world applicability, opportunities, and limitations for future integration with advanced driver assistance technologies.

scholarly journals A Survey on Road Traffic Sign Recognition System using Convolution Neural Network

2018 ◽  
Vol 8 (01) ◽  
Author(s):  
Yuga Hatolkar ◽  
Poorva Agarwal ◽  
Seema Patil
Keyword(s):  
Neural Network ◽  
Image Recognition ◽  
Traffic Accidents ◽  
Road Traffic ◽  
Recognition System ◽  
Convolution Neural Network ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Serious Injuries

Road Traffic accidents is one of the major reason for deaths taking place in India. These accidents not only result into serious injuries but may also lead to deaths. Image recognition technology is one of the widely used techniques used in various fields in research like agriculture, medicine, automobile etc. At present, majority of the Image recognition techniques use artificial feature extraction technique which is not only time consuming but also is very complex. Hence, various researchers are basically working in order to improve the algorithms, and make them more and more efficient and robust. Initially, traditional principle of convolution neural network was introduced briefly. Its numerous applications in the domain of Image Processing were presented. Finally, the challenges faced by Convolution Neural Network in terms of time complexity and accuracy were analyzed, and then our recent work was introduced in order to overcome the efficiency related issues.

scholarly journals Yolo V4 for advanced Traffic Sign Recognition with synthetic training data generated by various GAN

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Christine Dewi ◽  
Rung-Ching Chen ◽  
Yan-Ting Liu ◽  
Xiaoyi Jiang ◽  
Kristoko Dwi Hartomo
Keyword(s):  
Training Data ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Synthetic Training Data

An Approach of Traffic Sign Recognition Algorithm on MATLAB

2014 ◽  
Vol 644-650 ◽  
pp. 3980-3983
Author(s):  
Jia Yang Li ◽  
Mei Xia Song
Keyword(s):  
Real Time ◽  
Intelligent Transportation System ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign recognition system is a great important part of intelligent transportation system and advanced auxiliary driving system, and it is a key problem to improve the accuracy and real-time performance of traffic sign detection in reality.Considering to the perspective of accuracy and real-time of traffic sign detection and recognition, this article built the traffic sign detection and recognition method based on MATLAB. Finally, the paper proved the conclusion, and future traffic sign detection and recognition need to be further research topics and practical application prospect.

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