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.

Traffic Sign Recognition Based on Improved Deep Convolution Neural Network

2018 ◽  
Vol 55 (12) ◽  
pp. 121009 ◽  
Author(s):  
马永杰 Ma Yongjie ◽  
李雪燕 Li Xueyan ◽  
宋晓凤 Song Xiaofeng
Keyword(s):  
Neural Network ◽  
Convolution Neural Network ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Deep Convolution Neural Network

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 Research on traffic sign recognition method based on multi-scale convolution neural network

2021 ◽  
Vol 39 (4) ◽  
pp. 891-900
Author(s):  
Tiancheng Wei ◽  
Xiaofeng Chen ◽  
Yuanliang Yin
Keyword(s):  
Neural Network ◽  
Prior Knowledge ◽  
Target Recognition ◽  
Convolution Neural Network ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Data Set ◽  
Multi Scale ◽  
Sign Recognition ◽  
Recognition Ability

In order to accurately identify the traffic sign information under different road conditions, an improved deep learning method based on Faster RCNN model is proposed. Firstly, a multi-channel parallel full convolution neural network is designed to extract the color, shape and texture features of traffic signs in the original image. The multi-channel feature layers are fused to get the final feature map, and the adaptability of the model in various environment and weather conditions is enhanced by the image preprocessing. At the same time, the fusion features of deep and shallow feature layer are added into the feature extraction network, and the detailed texture information of shallow feature layer and semantic information of deep feature layer are retained, and the final feature layer can adapt to multi-scale change of traffic sign recognition. Secondly, the prior knowledge of traffic signs is used to detect and locate the target before the original RPN candidate region is generated. A more reasonable method for generating feature points and candidate anchor frames for traffic sign recognition is proposed. Based on the prior knowledge statistics of traffic sign size and proportion results, a target candidate frame suitable for traffic sign recognition is designed, a large number of redundant and negative correlation candidate frames is reduced, the detection accuracy and reduces the detection time is improved; secondly, the multi-scale candidate frame generation method for the deep and shallow feature layer is added to enhance the multi-scale target recognition ability and further strengthen the multi-scale target recognition ability Finally, this paper uses the international general traffic sign specification data set GTSRB/GTSDB and domestic traffic sign data set tt100k to verify the recognition ability of the model.

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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