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Application of Deep Learning to Autonomous Robotic Car

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2021
Oluwagbemiga O. Shoewu, Samuel O. Adebayo, Oluwafemi J. Ayangbekun, Lateef A. Akinyemi

Oluwagbemiga O Shoewu, Samuel O Adebayo, Oluwafemi J Ayangbekun and Lateef A Akinyemi. Application of Deep Learning to Autonomous Robotic Car. International Journal of Computer Applications 183(13):12-16, July 2021. BibTeX

	author = {Oluwagbemiga O. Shoewu and Samuel O. Adebayo and Oluwafemi J. Ayangbekun and Lateef A. Akinyemi},
	title = {Application of Deep Learning to Autonomous Robotic Car},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2021},
	volume = {183},
	number = {13},
	month = {Jul},
	year = {2021},
	issn = {0975-8887},
	pages = {12-16},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2021921421},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Autonomous machines are becoming prevalent, even more so the advent of autonomous vehicles. While autonomous cars have been around for some time, the endless innovations in this domain have led to the removal of human-in-the loop, hence constantly seeking to remove human input while delivering optimal result. However, safety is a major concern, and users are wary of leaving safety level decisions to machines. There is a rise in road accident caused by autonomous cars, while some have blamed it on human’s total trust in machines, and researchers have called for the development of human-level accurate algorithms to tackle decision making using state-of-the-art techniques. Therefore, this paper seeks to use computer vision leveraging on deep learning techniques to detect pedestrians, traffic signs, important objects, and lane lines to infer crucial driver decisions.Mask R-Convolutional Neural Network (CNN) was used for object classification with the aid of transfer learning saving the hassle of training and GPU times. A simple method for collecting data was applied using a wide-anglecamera and using Google TPU to perform real time object recognition without the need for a GPU enabled machine.


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Deep Learning (DL), Robotic, Autonomous, Object detection, Object recognition, Motor