Efficient convNets for fast traffic sign recognition
Open Access
- 10 April 2019
- journal article
- research article
- Published by Institution of Engineering and Technology (IET) in IET Intelligent Transport Systems
- Vol. 13 (6), 1011-1015
- https://doi.org/10.1049/iet-its.2018.5489
Abstract
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.Keywords
Funding Information
- National Natural Science Foundation of China (61702010)
This publication has 10 references indexed in Scilit:
- MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-Time Embedded Traffic Sign ClassificationIEEE Access, 2018
- ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile DevicesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2018
- Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methodsNeural Networks, 2018
- Xception: Deep Learning with Depthwise Separable ConvolutionsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- Aggregated Residual Transformations for Deep Neural NetworksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- A practical approach for detection and classification of traffic signs using Convolutional Neural NetworksRobotics and Autonomous Systems, 2016
- Convolutional neural networks at constrained time costPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2015
- Traffic Sign Recognition With Hinge Loss Trained Convolutional Neural NetworksIEEE Transactions on Intelligent Transportation Systems, 2014
- Multi-column deep neural network for traffic sign classificationNeural Networks, 2012
- Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognitionNeural Networks, 2012