Traffic Object Detection and Recognition Systems
DOI:
https://doi.org/10.32628/CSEIT24104110Keywords:
Convolutional Neural Networks, Traffic Sign Recognition, Image Processing, Automatic VehicleAbstract
You are already known about automatic vehicles in which the car can control itself. Cars must clearly understand and recognize all traffic signals. Many organizations named Uber, Google, Tesla, Toyota, Mercedes-Benz, Ford, Audi and others are getting involved on this technology to enhance their experience by adding features like autonomous driving and putting efforts in maximum innovation in this field. As a result, if we want to work with this technology accurately it depends on how the vehicle can distinguish between different signs such as no entry, height limit, turning signs, school signs, hospital signs, and many others. Traffic sign recognition is the process of differentiating the traffic signals into similar classes. Here we created a deep-neural-network system that can differentiate traffic signs. Using this system, we can analyze and process different traffic signals which plays a major role in all automatic vehicles. By using CNN, we propose an automated system for traffic sign detection, firstly conversion of original image to grey scale image takes place with the help of some vector machines used there, after that the convolutional-neural network is applied with limited and learnable layer for analyzing. Here it tries to crop the image boundary as per the original have.
Downloads
References
Simonyan K, Zisserman A (2014) "Very deep convolutional networks for large-scale image recognition." ArXiv Prepr.
Anand Rajaraman and Jeffrey David Ullman, "Mining of Massive Datasets," Cmbridge University Press.
Bill Franks, "Taming the Big Data Tidal wave: Finding Opportunities in Huge Data Streams with Advanced Analytics," John Wiley & Sons.
V. Vursov, S. Bibkov, P. Yakimov, "Localization of objects contours with different scales in images using Hough transform [in Russian]," Computer Optics, 37, 4(2013) 502-508. DOI: https://doi.org/10.18287/0134-2452-2013-37-4-496-502
P. Yakimov, "Tracking traffic signs in video sequences based on a vehicle velocity [in Russian]," Computer Optics, 39, 5(2015) 795-800. DOI: https://doi.org/10.18287/0134-2452-2015-39-5-795-800
J. Stallkamp, M. Schlipsing, J. Salmen, C. Igel, "Man vs. computer. Benchmarking machine learning algorithms for traffic sign recognition, Neural networks." 32 (2012) 323-332. DOI: https://doi.org/10.1016/j.neunet.2012.02.016
S. Houben, J. Stallkamp, J. Salmen, M. Schlipsing, C. Igel, "Detection of Traffic Signs in Real-World Images: The {G}erman {T}raffic {S}ign {D}etection {B}enchmark, in: Proc." International Joint Conference on Neural Networks, 2013. DOI: https://doi.org/10.1109/IJCNN.2013.6706807
Z.Zhu.D. Liang, S. Zhang, X. Huang, B.Li, S.Hu, "Trffic-Sign Detection and Classification in the Wild." Proceedings of CVPR, 2016, pp. 2110-2118. DOI: https://doi.org/10.1109/CVPR.2016.232
Y. LeCun, P. Sermanet, "Traffic Sign Recognition with Multi-Scale Covolutional Networks," Proceedings of Internaitonal Joint Conference on Neural Networks (IJCNN'11), 2011. DOI: https://doi.org/10.1109/IJCNN.2011.6033589
Arnold TB (2017) "KerasR: r interface to the keras deep learning library." J.Open Sorue Software 2(14): 296. DOI: https://doi.org/10.21105/joss.00296
Downloads
Published
Issue
Section
License
Copyright (c) 2024 International Journal of Scientific Research in Computer Science, Engineering and Information Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
How to Cite
