Open AccessTraffic sign recognition using weighted multi‐convolutional neural network
Author(s) -
Natarajan Sudha,
Annamraju Abhishek Kumar,
Baradkar Chaitree Sham
Publication year - 2018
Publication title -
iet intelligent transport systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.579
H-Index - 45
eISSN - 1751-9578
pISSN - 1751-956X
DOI - 10.1049/iet-its.2018.5171
Subject(s) - traffic sign recognition , convolutional neural network , computer science , traffic sign , classifier (uml) , artificial intelligence , benchmark (surveying) , reliability (semiconductor) , pattern recognition (psychology) , sign (mathematics) , mathematical analysis , mathematics , power (physics) , physics , geodesy , quantum mechanics , geography
Traffic signs play a crucial role in regulating traffic and facilitating cautious driving. Automatic traffic sign recognition is one of the key tasks in autonomous driving. Accuracy in the classification of traffic signs is therefore very important for the navigation of a vehicle. Here, a reliable and robust convolutional neural network (CNN) is presented for classifying these signs. The proposed classifier is a weighted multi‐CNN trained with a novel methodology. It achieves a near state‐of‐the‐art recognition rate of 99.59% when tested on the German traffic sign recognition benchmark dataset. Compared to the existing classifiers, the proposed one is a low‐complexity network that recognises a test image in 10 ms when running on an NVIDIA 980 Ti GPU system. The results demonstrate its suitability and reliability in high‐speed driving scenarios.