Open AccessEfficient convNets for fast traffic sign recognition
Author(s) -
Luo Xiaoping,
Zhu Jinhao,
Yu Qingying
Publication year - 2019
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.2018.5489
Subject(s) - traffic sign recognition , computation , benchmark (surveying) , sign (mathematics) , convolutional neural network , computer science , convolution (computer science) , artificial intelligence , traffic sign , pattern recognition (psychology) , data mining , algorithm , artificial neural network , mathematics , mathematical analysis , geodesy , geography
While deep convolutional networks gain overwhelming accuracy for computer vision, they are also well‐known for their high computation costs and memory demands. Given limited resources, they are difficult to apply. As a consequence, it is beneficial to investigate small, lightweight, accurate deep convolutional neural networks (ConvNets) that are better suited for resource‐limited electronic devices. This study presents qNet and sqNet, two small and efficient ConvNets for fast traffic sign recognition using uniform macro‐architecture and depth‐wise separable convolution. The qNet is designed with fewer parameters for even better accuracy. It possesses only 0.29M parameters (0.6 of one of the smallest models), while achieving a better accuracy of 99.4% on the German Traffic Sign Recognition Benchmark (GTSRB). The resulting sqNet possesses only 0.045M parameters (almost 0.1 of one of the smallest models) and 7.01M multiply‐add computations (reducing computations to 30% of one of the smallest models), while keeping an accuracy of 99% on the benchmark. The experimental results on the GTSRB demonstrate that authors’ networks are more efficient in using parameters and computations.