An approach towards efficient detection and recognition of traffic signs in videos using neural networks

2016 ◽  
Author(s):  
Supreeth H.S.G ◽  
Chandrashekar M Patil
Keyword(s):  
Neural Networks ◽  
Traffic Signs ◽  
Efficient Detection ◽  
Detection And Recognition

Detection and recognition of text traffic signs above the road

2021 ◽  
Vol 35 (2) ◽  
pp. 69
Author(s):  
Wei Sun ◽  
Yangtao Du ◽  
Xu Zhang ◽  
Guoce Zhang
Keyword(s):  
Traffic Signs ◽  
The Road ◽  
Detection And Recognition

scholarly journals Comparative Analysis on Deep Learning Approaches for Heavy-Vehicle Detection based on Data Augmentation and Transfer-Learning techniques

2021 ◽  
Vol 13 (3) ◽  
pp. 809-820
Author(s):  
V. Sowmya ◽  
R. Radha
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Real Time ◽  
Transfer Learning ◽  
Convolutional Neural Networks ◽  
Traffic Management ◽  
Data Augmentation ◽  
Vehicle Detection ◽  
Heavy Vehicles ◽  
Detection And Recognition

Vehicle detection and recognition require demanding advanced computational intelligence and resources in a real-time traffic surveillance system for effective traffic management of all possible contingencies. One of the focus areas of deep intelligent systems is to facilitate vehicle detection and recognition techniques for robust traffic management of heavy vehicles. The following are such sophisticated mechanisms: Support Vector Machine (SVM), Convolutional Neural Networks (CNN), Regional Convolutional Neural Networks (R-CNN), You Only Look Once (YOLO) model, etcetera. Accordingly, it is pivotal to choose the precise algorithm for vehicle detection and recognition, which also addresses the real-time environment. In this study, a comparison of deep learning algorithms, such as the Faster R-CNN, YOLOv2, YOLOv3, and YOLOv4, are focused on diverse aspects of the features. Two entities for transport heavy vehicles, the buses and trucks, constitute detection and recognition elements in this proposed work. The mechanics of data augmentation and transfer-learning is implemented in the model; to build, execute, train, and test for detection and recognition to avoid over-fitting and improve speed and accuracy. Extensive empirical evaluation is conducted on two standard datasets such as COCO and PASCAL VOC 2007. Finally, comparative results and analyses are presented based on real-time.

Detection and recognition of text traffic signs above the road

2021 ◽  
Vol 35 (2) ◽  
pp. 69
Author(s):  
Yangtao Du ◽  
Xu Zhang ◽  
Guoce Zhang ◽  
Wei Sun
Keyword(s):  
Traffic Signs ◽  
The Road ◽  
Detection And Recognition
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