Classification of Traffic Signs with Convolutional Neural Network
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Keywords:
Traffic Sign, Driver Safety, Convolutional Neural Network, Preprocessing, Computer VisionAbstract
Rapidly changing and developing technology and related investments are growing rapidly. Despite the rapid development of technology, there are problems that continue as a problem. It is necessary to deal with the problems that are still seen as a problem today by taking advantage of the possibilities and opportunities of technology. Traffic signs should be distinguished by drivers both at night and during the day and should be easily perceived in terms of life safety. At the same time, artificial intelligence supported solutions should be produced in order to reduce vehicle accidents caused by human errors. In order to achieve this, a CNN-based deep learning model suitable for real-time work has been proposed. The running performance of the proposed deep learning model was measured according to the raw input and preprocessed image type. According to the KFold 3 technique, training and test data were separated and the proposed model was trained. As a result of the experimental studies, 99% precision, recall, F1 score, and accuracy measurement metrics were achieved with the CNN model with preprocessed input type. According to the raw input type that has not undergone any preprocessing, success rates of 98%, 97%, 98%, and 98% were achieved in terms of precision, recall, F1 score, and accuracy metrics, respectively.
References
H. Xu and G. Srivastava, “Automatic recognition algorithm of traffic signs based on convolution neural network,” Multimed. Tools Appl., vol. 79, no. 17, pp. 11551–11565, 2020.
J. Cao, C. Song, S. Peng, F. Xiao, and S. Song, “Improved traffic sign detection and recognition algorithm for intelligent vehicles,” Sensors, vol. 19, no. 18, p. 4021, 2019.
J. Stallkamp, M. Schlipsing, J. Salmen, and C. Igel, “Man vs. computer: Benchmarking machine learning algorithms for traffic sign recognition,” Neural Networks, vol. 32, pp. 323–332, 2012, doi: https://doi.org/10.1016/j.neunet.2012.02.016.
A. De La Escalera, L. E. Moreno, M. A. Salichs, and J. M. Armingol, “Road traffic sign detection and classification,” IEEE Trans. Ind. Electron., vol. 44, no. 6, pp. 848–859, 1997.
Á. Arcos-García, J. A. Álvarez-García, and L. M. Soria-Morillo, “Deep neural network for traffic sign recognition systems: An analysis of spatial transformers and stochastic optimisation methods,” Neural Networks, vol. 99, pp. 158–165, 2018, doi: https://doi.org/10.1016/j.neunet.2018.01.005.
A. K. Jayaraman, A. Murugappan, T. E. Trueman, and E. Cambria, “Comment toxicity detection via a multichannel convolutional bidirectional gated recurrent unit,” Neurocomputing, vol. 441, pp. 272–278, 2021, doi: 10.1016/j.neucom.2021.02.023.
A. Z. da Costa, H. E. H. Figueroa, and J. A. Fracarolli, “Computer vision based detection of external defects on tomatoes using deep learning,” Biosyst. Eng., vol. 190, pp. 131–144, 2020, doi: https://doi.org/10.1016/j.biosystemseng.2019.12.003.
M. Sardogan, A. Tuncer, and Y. Ozen, “Plant Leaf Disease Detection and Classification Based on CNN with LVQ Algorithm,” in 2018 3rd International Conference on Computer Science and Engineering (UBMK), 2018, pp. 382–385. doi: 10.1109/UBMK.2018.8566635.
N. C. Mithun, S. Das, and S. A. Fattah, “Automated detection of optic disc and blood vessel in retinal image using morphological, edge detection and feature extraction technique,” in 16th Int’l Conf. Computer and Information Technology, Mar. 2014, no. March, pp. 98–102. doi: 10.1109/ICCITechn.2014.6997365.
S. Shuleenda Devi, S. A. Sheikh, A. Talukdar, and R. H. Laskar, “Malaria Infected Erythrocyte Classification Based on the Histogram Features using Microscopic Images of Thin Blood Smear,” Indian J. Sci. Technol., vol. 9, no. 45, Dec. 2016, doi: 10.17485/ijst/2016/v9i45/94119.
K. Wisaeng and W. Sangiamvibool, “Automatic detection and recognition of optic disk with maker-controlled watershed segmentation and mathematical morphology in color retinal images,” Soft Comput., vol. 22, no. 19, pp. 6329–6339, 2018, doi: 10.1007/s00500-017-2681-9.
K. He, G. Gkioxari, P. Dollár, and R. Girshick, “Mask r-cnn,” in Proceedings of the IEEE international conference on computer vision, 2017, pp. 2961–2969.
C. Bahlmann, Y. Zhu, V. Ramesh, M. Pellkofer, and T. Koehler, “A system for traffic sign detection, tracking, and recognition using color, shape, and motion information,” in IEEE Proceedings. Intelligent Vehicles Symposium, 2005., 2005, pp. 255–260.
C. G. Keller, C. Sprunk, C. Bahlmann, J. Giebel, and G. Baratoff, “Real-time recognition of US speed signs,” in 2008 IEEE Intelligent Vehicles Symposium, 2008, pp. 518–523.
S. M. Bascón, J. A. Rodríguez, S. L. Arroyo, A. F. Caballero, and F. López-Ferreras, “An optimization on pictogram identification for the road-sign recognition task using SVMs,” Comput. Vis. Image Underst., vol. 114, no. 3, pp. 373–383, 2010.
Q. Hu, S. Paisitkriangkrai, C. Shen, A. van den Hengel, and F. Porikli, “Fast detection of multiple objects in traffic scenes with a common detection framework,” IEEE Trans. Intell. Transp. Syst., vol. 17, no. 4, pp. 1002–1014, 2015.
D. Temel, M.-H. Chen, and G. AlRegib, “Traffic sign detection under challenging conditions: A deeper look into performance variations and spectral characteristics,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 9, pp. 3663–3673, 2019.
U. Kamal, T. I. Tonmoy, S. Das, and M. K. Hasan, “Automatic Traffic Sign Detection and Recognition Using SegU-Net and a Modified Tversky Loss Function With L1-Constraint,” IEEE Trans. Intell. Transp. Syst., vol. 21, no. 4, pp. 1467–1479, 2020, doi: 10.1109/TITS.2019.2911727.
R. Castruita Rodríguez, C. Mendoza Carlos, O. O. Vergara Villegas, V. G. Cruz Sánchez, and H. de J. Ochoa Domínguez, “Mexican traffic sign detection and classification using deep learning,” Expert Syst. Appl., vol. 202, p. 117247, 2022, doi: https://doi.org/10.1016/j.eswa.2022.117247.
W. Liu, Y. Xue, Y. Tang, and A. Sun, “A Self-regularized Low-light Image Enhancement Based on the Brightness Information,” in 2022 4th International Conference on Natural Language Processing (ICNLP), 2022, pp. 119–124. doi: 10.1109/ICNLP55136.2022.00027.
A. Staravoitau, “Traffic Sign Classification with a Convolutional Network,” Pattern Recognit. Image Anal., vol. 28, no. 1, pp. 155–162, 2018, doi: 10.1134/S1054661818010182.
S. Naim and N. Moumkine, “LiteNet: A Novel Approach for Traffic Sign Classification Using a Light Architecture BT - WITS 2020,” 2022, pp. 37–47.
J. Mishra and S. Goyal, “An effective automatic traffic sign classification and recognition deep convolutional networks,” Multimed. Tools Appl., vol. 81, no. 13, pp. 18915–18934, 2022, doi: 10.1007/s11042-022-12531-w.
C. Prasanna, V. V. Kumar, and G. A. E. S. Kumar, “Traffic Sign Recognition Using CNN,” in 2022 IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics (ICDCECE), 2022, pp. 1–5.
K. Xie, S. Ge, R. Yang, X. Lu, and L. Sun, “Negative-Supervised Cascaded Deep Learning for Traffic Sign Classification BT - Computer Vision,” 2015, pp. 249–257.
