Abstract
Traffic sign recognition is an essential phase for intelligent autonomous driving systems. In this work, we have presented a recognition solution which is based on the classification performed by supervised learning method through deep learning an artificial using Convolutional Neural Network (CNN). Three pre-trained neural networks such as, GoogLeNet, VGG-16, AlexNet, were adapted for transfer learning algorithm. In order to improve the classification accuracy and to solve the problem of the insufficient amount of training dataset or the unequal number of images for each class, we have used the data augmentation technique applied on German Traffic Sign Benchmark dataset (GTSRB). The obtained experimental results show that the size of the dataset as the number of training images has a significant impact on the classification accuracy of the CNN model. The number of images was increased from 51,840 to 96,750 images by using data augmentation technique. Therefore the classification accuracy was increased from 86.72% to 97.33%.
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References
Albawi, S., Mohammed, T.A., Al-Zawi, S.: Understanding of a convolutional neural network. In: 2017 International Conference on Engineering and Technology (ICET), August 2017, pp. 1–6 (2017). https://doi.org/10.1109/ICEngTechnol.2017.8308186
Shorten, C., Khoshgoftaar, T.M.: A survey on image data augmentation for deep learning. J. Big Data 6(1), 1–48 (2019). https://doi.org/10.1186/s40537-019-0197-0
Nasri, I., Messaoudi, A., Kassmi, K., Karrouchi, M., Snoussi, H.: Adaptive fine-tuning for deep transfer learning based traffic signs classification. In: 2021 4th International Symposium on Advanced Electrical and Communication Technologies (ISAECT), December 2021 pp. 1–5 (2021). https://doi.org/10.1109/ISAECT53699.2021.9668592
Mikołajczyk, A., Grochowski, M.: Data augmentation for improving deep learning in image classification problem. In: 2018 International Interdisciplinary Ph.D. Workshop (IIPhDW), May 2018, pp. 117–122 (2018). https://doi.org/10.1109/IIPHDW.2018.8388338
Fawzi, A., Samulowitz, H., Turaga, D., Frossard, P.: Adaptive data augmentation for image classification. In: 2016 IEEE International Conference on Image Processing (ICIP), September 2016, pp. 3688–3692 (2016). https://doi.org/10.1109/ICIP.2016.7533048
Stallkamp, J., Schlipsing, M., Salmen, J., Igel, C.: The German traffic sign recognition benchmark: a multi-class classification competition. In: The 2011 International Joint Conference on Neural Networks, July 2011, pp. 1453–1460 (2011). https://doi.org/10.1109/IJCNN.2011.6033395
Timofte, R., Zimmermann, K., Van Gool, L.: Multi-view traffic sign detection, recognition, and 3D localisation. Mach. Vis. Appl. 25(3), 633–647 (2011). https://doi.org/10.1007/s00138-011-0391-3
Larsson, F., Felsberg, M.: Using Fourier descriptors and spatial models for traffic sign recognition. In: Heyden, A., Kahl, F. (eds.) SCIA 2011. LNCS, vol. 6688, pp. 238–249. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21227-7_23
Møgelmose, A., Trivedi, M.M., Moeslund, T.B.: Learning to detect traffic signs: comparative evaluation of synthetic and real-world datasets. In: Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), November 2012, pp. 3452–3455 (2012)
LeCun, Y., et al.: Handwritten digit recognition with a back-propagation network. Adv. Neural Inf. Process. Syst. vol. 2 (1989)
LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (1989). https://doi.org/10.1162/neco.1989.1.4.541
Lecun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998). https://doi.org/10.1109/5.726791
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386
Deng, J., et al.: ImageNet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, June 2009, pp. 248–255 (2009). https://doi.org/10.1109/CVPR.2009.5206848
Nasri, I., Karrouchi, M., Snoussi, H., Kassmi, K., Messaoudi, A.: DistractNet: a deep convolutional neural network architecture for distracted driver classification. IAES Int. J. Artif. Intell. IJ-AI 11(2), 494–503 (2022). https://doi.org/10.11591/ijai.v11.i2.pp494-503
Shaha, M., Pawar, M.: Transfer learning for image classification. In: 2018 Second International Conference on Electronics, Communication and Aerospace Technology (ICECA), March 2018, pp. 656–660 (2018). https://doi.org/10.1109/ICECA.2018.8474802
Nasri, I., Karrouchi, M., Snoussi, H., Kassmi, K., Messaoudi, A.: Detection and prediction of driver drowsiness for the prevention of road accidents using deep neural networks techniques. In: Bennani, S., Lakhrissi, Y., Khaissidi, G., Mansouri, A., Khamlichi, Y. (eds.) WITS 2020. LNEE, vol. 745, pp. 57–64. Springer, Singapore (2022). https://doi.org/10.1007/978-981-33-6893-4_6
Szegedy, C., et al.: Going deeper with convolutions. In: 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2015, pp. 1–9 (2015). https://doi.org/10.1109/CVPR.2015.7298594
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations (2014). https://doi.org/10.48550/arXiv.1409.1556
Zhou, B., Lapedriza, A., Torralba, A., Oliva, A.: Places: an image database for deep scene understanding. J. Vis. 17(10), 296 (2017). https://doi.org/10.1167/17.10.296
Goutte, C., Gaussier, E.: A probabilistic interpretation of precision, recall and F-score, with implication for evaluation. In: Losada, D.E., Fernández-Luna, J.M. (eds.) ECIR 2005. LNCS, vol. 3408, pp. 345–359. Springer, Heidelberg (2005). https://doi.org/10.1007/978-3-540-31865-1_25
Nasri, I., Karrouchi, M., Snoussi, H., Messaoudi, A., Kassmi, K.: MaskNet: CNN for real-time face mask detection based on deep learning techniques. In: Motahhir, S., Bossoufi, B. (eds.) ICDTA 2021. LNNS, vol. 211, pp. 87–97. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-73882-2_9
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Nasri, I., Karrouchi, M., Messaoudi, A., Kassmi, K., Zerouali, S. (2023). Data Augmentation and Deep Learning Applied for Traffic Signs Image Classification. In: Motahhir, S., Bossoufi, B. (eds) Digital Technologies and Applications. ICDTA 2023. Lecture Notes in Networks and Systems, vol 669. Springer, Cham. https://doi.org/10.1007/978-3-031-29860-8_12
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