Intelligent Transport System: Classification of Traffic Signs Using Deep Neural Networks in Real Time
Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.
[1] Priya Garg, Debapriyo Roy Chowdhury and Vidya N. More, “Traffic Sign Recognition and Classification Using YOLOv2, Faster RCNN and SSD,” In: 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) 2019, December 2019.
[2] Evan Peng, Feng Chen, and Xinkai Song, “Traffic Sign Detection with Convolutional Neural Networks,” In International Conference on Cognitive Systems and Signal Processing, July 2017.
[3] Wang Canyong, “Research and Application of Traffic Sign Detection and Recognition based on Deep Learning,” In: International Conference on Robots & Intelligent System (ICRIS) 2018, May 2018.
[4] Xiong Changzhen, Wang Cong, Ma Weixin, Shan Yanmei, “A Traffic Sign Detection Algorithm Based on Deep Convolutional Neural Network,” In: IEEE International Conference on Signal and Image Processing (ICSIP) 2016, March 2017.
[5] Mao, X., Hijazi, S., Casas, R., Kaul, P., Kumar, R., Rowen, C.: Hierarchical CNN for traffic sign recognition. In: Intelligent Vehicles Symposium (IV), 2016 IEEE, pp. 130–135. IEEE (2016).
[6] Qian, R., Yue, Y., Coenen, F., Zhang, B.: Traffic sign recognition with convolutional neural network based on max pooling positions. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 578–582. IEEE (2016).
[7] Zeng, Y., Xu, X., Fang, Y., Zhao, K.: Traffic sign recognition using extreme learning classifier with deep convolutional features. In: The 2015 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2015), Suzhou, China, vol. 9242, pp. 272–280 (2015).
[8] Zeng, Y., Xu, X., Fang, Y., & Zhao, K. (2015). Traffic sign recognition using deep convolutional networks and extreme learning machine. In Intelligence science and big data engineering. image and video data engineering (IScIDE ) (pp. 272–280). Springer. doi:10.1007/ 978-3-319-23989-7_28.
[9] Dan Cireşan, Ueli Meier, Jonathan Masci, and Jürgen Schmidhuber, “Multi-column deep neural network for traffic sign classification,” In Neural Networks, Elsevier, August 2012.
[10] Ciresan, D., Meier, U., Masci, J., Schmidhuber, J., “A committee of neural networks for traffic sign classification,” In: Dalle Molle Institute for Artificial Intelligence (2011).
[11] Uday Kamal, Sowmitra Das, Abid Abrar, and Md. Kamrul Hasan, “Traffic-Sign Detection and Classification Under Challenging Conditions: A Deep Neural Network Based Approach,” In: IEEE Video and Image Processing Cup 2017, September 2017.
[12] Hamed Habibi Aghdam, Elnaz Jahani Heravi, Domenec Puig, “A Practical and Highly Optimized Convolutional Neural Network for Classifying Traffic Signs in Real-Time,” In International Journal of Computer Vision, September 2016.
[13] Fleyeh, H., & Davami, E. (2011). Eigen-based traffic sign recognition. IET Intelligent Transport Systems, 5(3), 190. doi:10.1049/iet-its. 2010.0159.
[14] J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. (2011). The GermanTraffic Sign Recognition Benchmark: A multi-class classification com-petition. In: International Joint Conference on Neural Networks.
[15] National Highway Traffic Safety Administration, Traffic Safety Facts: Alcohol-Impaired Driving, 2019 Data, DOT HS 812 360, 2019
[1] Priya Garg, Debapriyo Roy Chowdhury and Vidya N. More, “Traffic Sign Recognition and Classification Using YOLOv2, Faster RCNN and SSD,” In: 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT) 2019, December 2019.
[2] Evan Peng, Feng Chen, and Xinkai Song, “Traffic Sign Detection with Convolutional Neural Networks,” In International Conference on Cognitive Systems and Signal Processing, July 2017.
[3] Wang Canyong, “Research and Application of Traffic Sign Detection and Recognition based on Deep Learning,” In: International Conference on Robots & Intelligent System (ICRIS) 2018, May 2018.
[4] Xiong Changzhen, Wang Cong, Ma Weixin, Shan Yanmei, “A Traffic Sign Detection Algorithm Based on Deep Convolutional Neural Network,” In: IEEE International Conference on Signal and Image Processing (ICSIP) 2016, March 2017.
[5] Mao, X., Hijazi, S., Casas, R., Kaul, P., Kumar, R., Rowen, C.: Hierarchical CNN for traffic sign recognition. In: Intelligent Vehicles Symposium (IV), 2016 IEEE, pp. 130–135. IEEE (2016).
[6] Qian, R., Yue, Y., Coenen, F., Zhang, B.: Traffic sign recognition with convolutional neural network based on max pooling positions. In: 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD), pp. 578–582. IEEE (2016).
[7] Zeng, Y., Xu, X., Fang, Y., Zhao, K.: Traffic sign recognition using extreme learning classifier with deep convolutional features. In: The 2015 International Conference on Intelligence Science and Big Data Engineering (IScIDE 2015), Suzhou, China, vol. 9242, pp. 272–280 (2015).
[8] Zeng, Y., Xu, X., Fang, Y., & Zhao, K. (2015). Traffic sign recognition using deep convolutional networks and extreme learning machine. In Intelligence science and big data engineering. image and video data engineering (IScIDE ) (pp. 272–280). Springer. doi:10.1007/ 978-3-319-23989-7_28.
[9] Dan Cireşan, Ueli Meier, Jonathan Masci, and Jürgen Schmidhuber, “Multi-column deep neural network for traffic sign classification,” In Neural Networks, Elsevier, August 2012.
[10] Ciresan, D., Meier, U., Masci, J., Schmidhuber, J., “A committee of neural networks for traffic sign classification,” In: Dalle Molle Institute for Artificial Intelligence (2011).
[11] Uday Kamal, Sowmitra Das, Abid Abrar, and Md. Kamrul Hasan, “Traffic-Sign Detection and Classification Under Challenging Conditions: A Deep Neural Network Based Approach,” In: IEEE Video and Image Processing Cup 2017, September 2017.
[12] Hamed Habibi Aghdam, Elnaz Jahani Heravi, Domenec Puig, “A Practical and Highly Optimized Convolutional Neural Network for Classifying Traffic Signs in Real-Time,” In International Journal of Computer Vision, September 2016.
[13] Fleyeh, H., & Davami, E. (2011). Eigen-based traffic sign recognition. IET Intelligent Transport Systems, 5(3), 190. doi:10.1049/iet-its. 2010.0159.
[14] J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel. (2011). The GermanTraffic Sign Recognition Benchmark: A multi-class classification com-petition. In: International Joint Conference on Neural Networks.
[15] National Highway Traffic Safety Administration, Traffic Safety Facts: Alcohol-Impaired Driving, 2019 Data, DOT HS 812 360, 2019
@article{"International Journal of Information, Control and Computer Sciences:79883", author = "Anukriti Kumar and Tanmay Singh and Dinesh Kumar Vishwakarma", title = "Intelligent Transport System: Classification of Traffic Signs Using Deep Neural Networks in Real Time", abstract = "Traffic control has been one of the most common and irritating problems since the time automobiles have hit the roads. Problems like traffic congestion have led to a significant time burden around the world and one significant solution to these problems can be the proper implementation of the Intelligent Transport System (ITS). It involves the integration of various tools like smart sensors, artificial intelligence, position technologies and mobile data services to manage traffic flow, reduce congestion and enhance driver's ability to avoid accidents during adverse weather. Road and traffic signs’ recognition is an emerging field of research in ITS. Classification problem of traffic signs needs to be solved as it is a major step in our journey towards building semi-autonomous/autonomous driving systems. The purpose of this work focuses on implementing an approach to solve the problem of traffic sign classification by developing a Convolutional Neural Network (CNN) classifier using the GTSRB (German Traffic Sign Recognition Benchmark) dataset. Rather than using hand-crafted features, our model addresses the concern of exploding huge parameters and data method augmentations. Our model achieved an accuracy of around 97.6% which is comparable to various state-of-the-art architectures.
", keywords = "Multiclass classification, convolution neural network, OpenCV, Data Augmentation.", volume = "14", number = "10", pages = "324-6", }
