Abstract
Recently, the amount of research in the field of self-driving cars has grown significantly with autonomous vehicles having clocked in more than 10 million miles, providing a substantial amount of data for use in training and testing. The most complex part of training is the use of computer vision for feature extraction and object detection in real-time. Much relevant research has been done on improving the algorithms in the area of image segmentation. The proposed idea presents the use of Convoluted Neural Networks using Spatial Transformer Networks and lane detection in real time to increase the efficiency of autonomous vehicles. The depth of the neural network will help in training vehicles and during the testing phase, the vehicles will learn to make decisions based on the training data. In case of sudden changes to the environment, the vehicle will be able to make decisions quickly to prevent damage or danger to lives. Along with lane detection, a self-driving car must also be able to detect traffic signs. The proposed approach uses the Adam Optimizer which runs on top of the LeNet-5 architecture. The LeNet-5 architecture is analyzed and compared with the Feed Forward Neural Network approach. The accuracy of the LeNet-5 architecture was found to be 97% while the accuracy of the Feed Forward Neural Network was 94%.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aziz MVG, Prihatmanto AS, Hindersah H (2017) Implementation of lane detection algorithm for self-driving car on toll road cipularang using Python language. In: 2017 4th international conference on electric vehicular technology (ICEVT), pp 144–148. IEEE
Chatrati SP, Hossain G, Goyal A, Bhan A, Bhattacharya S, Gaurav D, Tiwari SM (2020) Smart home health monitoring system for predicting type 2 diabetes and hypertension. J King Saud Univ Comput Inform Sci 24:1–9
Cong G, Bhardwaj O (2017) A hierarchical, bulk-synchronous stochastic gradient descent algorithm for deep-learning applications on gpu clusters. In: 2017 16th IEEE international conference on machine learning and applications (ICMLA), pp 818–821. IEEE
Dorj B, Hossain S, Lee DJ (2020) Highly curved lane detection algorithms based on Kalman filter. Appl Sci 10(7):2372
Dwivedi R, Dey S, Chakraborty C, Tiwari S (2021) Grape disease detection network based on multi-task learning and attention features. IEEE Sens J
Gadekallu TR, Rajput DS, Reddy MPK, Lakshmanna K, Bhattacharya S, Singh S, Alazab M (2020) A novel PCA–whale optimization-based deep neural network model for classification of tomato plant diseases using GPU. J Real Time Image Process 493:1–14
Garimella G, Funke J, Wang C, Kobilarov M (2017) Neural network modeling for steering control of an autonomous vehicle. In: 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS), pp 2609–2615. IEEE
Gaurav D, Tiwari SM, Goyal A, Gandhi N, Abraham A (2020) Machine intelligence-based algorithms for spam filtering on document labeling. Soft Comput 24(13):9625–9638
Gupta T, Sikchi HS, Charkravarty D (2018) Robust lane detection using multiple features. In: 2018 IEEE intelligent vehicles symposium (IV), pp 1470–1475. IEEE
Kim S, Lee J, Kim Y (2016) Speed-adaptive ratio-based lane detection algorithm for self-driving vehicles. In: 2016 international SoC design conference (ISOCC), pp 269–270. IEEE
Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput vis 60(2):91–110
Mishra S, Sagban R, Yakoob A, Gandhi N (2018) Swarm intelligence in anomaly detection systems: an overview. Int J Comput Appl 43:1–10
Nugraha BT, Su SF (2017) Towards self-driving car using convolutional neural network and road lane detector. In: 2017 2nd international conference on automation, cognitive science, optics, micro electro-mechanical system, and information technology (ICACOMIT), pp 65–69. IEEE
OpenCV (Open Source Computer Vision Library). https://opencv.org. Accessed 4 April 2020
Park H (2018) Implementation of lane detection algorithm for self-driving vehicles using tensor flow. In: International conference on innovative mobile and internet services in ubiquitous computing, pp 438–447. Springer, Cham
Prabhakar G, Kailath B, Natarajan S, Kumar R (2017) Obstacle detection and classification using deep learning for tracking in high-speed autonomous driving. In: 2017 IEEE region 10 symposium (TENSYMP), pp 1–6. IEEE
Rahul M, Kohli N, Agarwal R, Mishra S (2019) Facial expression recognition using geometric features and modified hidden Markov model. Int J Grid Util Comput 10(5):488–496
Ren S, He K, Girshick R, Sun J (2015) Faster r-cnn: towards real-time object detection with region proposal networks. arXiv preprint arXiv:1506.01497
Renjith R, Reshma R, Arun KV (2017) Design and implementation of traffic sign and obstacle detection in a self-driving car using SURF detector and Brute force matcher. In: 2017 IEEE international conference on power, control, signals and instrumentation engineering (ICPCSI), pp 1985–1989. IEEE
Sant A, Garg L, Xuereb P, Chakraborty C (2021) A novel green IoT-based pay-as-you-go smart parking system. CMC Comput Mater Cont 67(3):3523–3544
Shapiro LG, Stockman GC (2001) Computer vision. Prentice Hall PTR, Upper Saddle River
Singh G, Chowdhary M, Kumar A, Bahl R (2020) A personalized classifier for human motion activities with semi-supervised learning. IEEE Trans Consum Electron 66(4):346–355
Stallkamp J, Schlipsing M, Salmen J, Igel C (2011) The German traffic sign recognition benchmark: a multi-class classification competition. In: The 2011 international joint conference on neural networks, pp 1453–1460. IEEE
Sucharitha M, Chakraborty C, Rao SS, Reddy VSK (2020) Computer vision for brain tissue segmentation. In: Green computing and predictive analytics for healthcare, pp 81–94. Chapman and Hall/CRC
Zhang W, Wu QJ, Yang X, Fang X (2008) Multilevel framework to detect and handle vehicle occlusion. IEEE Trans Intell Transp Syst 9(1):161–174
Acknowledgements
The authors would like to thank the researchers at the German Ruht-Universitaet Bochum in Germany for making available the very useful and comprehensive German Traffic Sign Dataset (http://benchmark.ini.rub.de/) that was used in this research (Stallkamp et al. 2011). This research was completed as a master's graduate research project at the Department of Electrical Engineering and Computer Science at Texas A&M University - Kingsville.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that there is no conflict of interest.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Kanagaraj, N., Hicks, D., Goyal, A. et al. Deep learning using computer vision in self driving cars for lane and traffic sign detection. Int J Syst Assur Eng Manag 12, 1011–1025 (2021). https://doi.org/10.1007/s13198-021-01127-6
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-021-01127-6