Improved VGG Model for Road Traffic Sign Recognition

Road traffic sign recognition is an important task in intelligent transportation system. Convolutional neural networks (CNNs) have achieved a breakthrough in computer vision tasks and made great success in traffic sign classification. In this paper, it presents a road traffic sign recognition algorithm based on a convolutional neural network. In natural scenes, traffic signs are disturbed by factors such as illumination, occlusion, missing and deformation, and the accuracy of recognition decreases, this paper proposes a model called Improved VGG (IVGG) inspired by VGG model. The IVGG model includes 9 layers, compared with the original VGG model, it is added max-pooling operation and dropout operation after multiple convolutional layers, to catch the main features and save the training time. The paper proposes the method which adds dropout and Batch Normalization (BN) operations after each fully-connected layer, to further accelerate the model convergence, and then it can get better classification effect. It uses the German Traffic Sign Recognition Benchmark (GTSRB) dataset in the experiment. The IVGG model enhances the recognition rate of traffic signs and robustness by using the data augmentation and transfer learning, and the spent time is also reduced greatly.

[1]  Tat-Seng Chua,et al.  NUS-WIDE: a real-world web image database from National University of Singapore , 2009, CIVR '09.

[2]  Ahmad Makui,et al.  Multi-attribute group decision making approach based on interval-valued intuitionistic fuzzy sets and evidential reasoning methodology , 2017, Soft Comput..

[3]  Domenec Puig,et al.  Recognizing Traffic Signs Using a Practical Deep Neural Network , 2015, ROBOT.

[4]  Bin Fan,et al.  Traffic Sign Recognition Using a Multi-Task Convolutional Neural Network , 2018, IEEE Transactions on Intelligent Transportation Systems.

[5]  David Hyunchul Shim,et al.  Real-time Traffic Sign Recognition system with deep convolutional neural network , 2016, 2016 13th International Conference on Ubiquitous Robots and Ambient Intelligence (URAI).

[6]  郁红陶,et al.  Automatic Recognition for Mechanical Images Based on Sparse Non-negative Matrix Factorization and Probabilistic Neural Networks , 2015 .

[7]  Brian Kingsbury,et al.  Very deep multilingual convolutional neural networks for LVCSR , 2015, 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[8]  Peter Glöckner,et al.  Why Does Unsupervised Pre-training Help Deep Learning? , 2013 .

[9]  Md. Abdul Alim Sheikh,et al.  Traffic sign detection and classification using colour feature and neural network , 2016, 2016 International Conference on Intelligent Control Power and Instrumentation (ICICPI).

[10]  Syed Muhammad Anwar,et al.  Deep Learning in Medical Image Analysis , 2017 .

[11]  Johannes Stallkamp,et al.  Detection of traffic signs in real-world images: The German traffic sign detection benchmark , 2013, The 2013 International Joint Conference on Neural Networks (IJCNN).

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

[13]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

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

[15]  Yann LeCun,et al.  Traffic sign recognition with multi-scale Convolutional Networks , 2011, The 2011 International Joint Conference on Neural Networks.

[16]  Marcin Skoczylas,et al.  Recognition of multiple traffic signs using keypoints feature detectors , 2016, 2016 International Conference and Exposition on Electrical and Power Engineering (EPE).

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

[18]  Makoto Yoshizawa,et al.  Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis , 2016, 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE).

[19]  Yoshua Bengio,et al.  Understanding the difficulty of training deep feedforward neural networks , 2010, AISTATS.

[20]  Jiannong Cao,et al.  A Distributed TCAM Coprocessor Architecture for Integrated Longest Prefix Matching, Policy Filtering, and Content Filtering , 2013, IEEE Transactions on Computers.

[21]  Dayong Shen,et al.  Traffic Sign Recognition Using Kernel Extreme Learning Machines With Deep Perceptual Features , 2017, IEEE Transactions on Intelligent Transportation Systems.

[22]  Yike Guo,et al.  Fast Traffic Sign Recognition with a Rotation Invariant Binary Pattern Based Feature , 2015, Sensors.