Improved VGG model-based efficient traffic sign recognition for safe driving in 5G scenarios

The rapid development and application of AI in intelligent transportation systems has widely impacted daily life. The application of an intelligent visual aid for traffic sign information recognition can provide assistance and even control vehicles to ensure safe driving. The field of autonomous driving is booming, and great progress has been made. Many traffic sign recognition algorithms based on convolutional neural networks (CNNs) have been proposed because of the fast execution and high recognition rate of CNNs. However, this work addresses a challenging question in the autonomous driving field: how can traffic signs be recognized in real time and accurately? The proposed method designs an improved VGG convolutional neural network and has significantly superior performance compared with existing schemes. First, some redundant convolutional layers are removed efficiently from the VGG-16 network, and the number of parameters is greatly reduced to further optimize the overall architecture and accelerate calculation. Furthermore, the BN (batch normalization) layer and GAP (global average pooling) layer are added to the network to improve the accuracy without increasing the number of parameters. The proposed method needs only 1.15 M when using the improved VGG-16 network. Finally, extensive experiments on the German Traffic Sign Recognition Benchmark (GTSRB) Dataset are performed to evaluate our proposed scheme. Compared with traditional methods, our scheme significantly improves recognition accuracy while maintaining good real-time performance.

[1]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Yucong Duan,et al.  The Cloud-edge-based Dynamic Reconfiguration to Service Workflow for Mobile Ecommerce Environments , 2021, ACM Trans. Internet Techn..

[3]  Mark Sandler,et al.  MobileNetV2: Inverted Residuals and Linear Bottlenecks , 2018, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[4]  Kavita Bala,et al.  Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[5]  Johannes Stallkamp,et al.  The German Traffic Sign Recognition Benchmark: A multi-class classification competition , 2011, The 2011 International Joint Conference on Neural Networks.

[6]  Sergey Ioffe,et al.  Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift , 2015, ICML.

[7]  Tieniu Tan,et al.  Practical Camera Calibration From Moving Objects for Traffic Scene Surveillance , 2013, IEEE Transactions on Circuits and Systems for Video Technology.

[8]  Martín Abadi,et al.  TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems , 2016, ArXiv.

[9]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[10]  Japan road sign classification using cascade convolutional neural network , 2016 .

[11]  Xu Sun,et al.  Adaptive Gradient Methods with Dynamic Bound of Learning Rate , 2019, ICLR.

[12]  Kilian Q. Weinberger,et al.  Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[13]  Jimmy Ba,et al.  Adam: A Method for Stochastic Optimization , 2014, ICLR.

[14]  Rob Fergus,et al.  Visualizing and Understanding Convolutional Networks , 2013, ECCV.

[15]  Luc Van Gool,et al.  Traffic sign recognition — How far are we from the solution? , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

[16]  Richard O. Duda,et al.  Use of the Hough transformation to detect lines and curves in pictures , 1972, CACM.

[17]  Qiang Ling,et al.  A background modeling and foreground segmentation approach based on the feedback of moving objects in traffic surveillance systems , 2014, Neurocomputing.

[18]  Jian Dong,et al.  Attentive Contexts for Object Detection , 2016, IEEE Transactions on Multimedia.

[19]  Jürgen Schmidhuber,et al.  A committee of neural networks for traffic sign classification , 2011, The 2011 International Joint Conference on Neural Networks.

[20]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[21]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[22]  Alexander Wong,et al.  MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-Time Embedded Traffic Sign Classification , 2018, IEEE Access.

[23]  Luming Zhang,et al.  Integrating 3D structure into traffic scene understanding with RGB-D data , 2015, Neurocomputing.

[24]  Baoli Li,et al.  Traffic-Sign Detection and Classification in the Wild , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[25]  StallkampJ.,et al.  2012 Special Issue , 2012 .

[26]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[27]  Xiang Bai,et al.  Learning Discriminative Pattern for Real-Time Car Brand Recognition , 2015, IEEE Transactions on Intelligent Transportation Systems.

[28]  CireşAnDan,et al.  2012 Special Issue , 2012 .

[29]  Xiang Li,et al.  Understanding the Disharmony Between Dropout and Batch Normalization by Variance Shift , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

[30]  Zhou Yu,et al.  Multimodal Transformer With Multi-View Visual Representation for Image Captioning , 2019, IEEE Transactions on Circuits and Systems for Video Technology.

[31]  Jun Yu,et al.  Hierarchical Deep Click Feature Prediction for Fine-Grained Image Recognition , 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Gang Sun,et al.  Squeeze-and-Excitation Networks , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.

[33]  Wenyu Liu,et al.  Human Detection Using Learned Part Alphabet and Pose Dictionary , 2014, ECCV.

[34]  Johannes Stallkamp,et al.  Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition , 2012, Neural Networks.

[35]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[36]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[37]  Xiangyu Zhang,et al.  ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design , 2018, ECCV.

[38]  Wei Liu,et al.  ParseNet: Looking Wider to See Better , 2015, ArXiv.

[39]  Sergey Ioffe,et al.  Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning , 2016, AAAI.

[40]  Jürgen Schmidhuber,et al.  Multi-column deep neural network for traffic sign classification , 2012, Neural Networks.

[41]  Changshui Zhang,et al.  Traffic Sign Recognition With Hinge Loss Trained Convolutional Neural Networks , 2014, IEEE Transactions on Intelligent Transportation Systems.

[42]  Sergey Ioffe,et al.  Rethinking the Inception Architecture for Computer Vision , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[43]  Honghao Gao,et al.  V2VR: Reliable Hybrid-Network-Oriented V2V Data Transmission and Routing Considering RSUs and Connectivity Probability , 2020, IEEE Transactions on Intelligent Transportation Systems.

[44]  Kaiqi Huang,et al.  View independent object classification by exploring scene consistency information for traffic scene surveillance , 2013, Neurocomputing.

[45]  Qiang Chen,et al.  Network In Network , 2013, ICLR.

[46]  Rui Li,et al.  Context-Aware QoS Prediction With Neural Collaborative Filtering for Internet-of-Things Services , 2020, IEEE Internet of Things Journal.

[47]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.