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Robust , fast and accurate lane departure warning system using deep learning and mobilenets

Olanrewaju, Rashidah Funke and Ahmad Fakhri, Ahmad Syarifuddin and Sanni, Mistura L. and Ajala, Mosud Taiwo (2019) Robust , fast and accurate lane departure warning system using deep learning and mobilenets. In: "7th International Conference on Mechatronics Engineering, ICOM 2019", 30 - 31 Oct. 2019, Putrajaya, Malaysia.

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Every year, millions of people die from fatalities on the road. This paper develops a lane departure warning system that will alert the driver when the driver may be veering off the road. Recent advances in Deep learning and Artificial Intelligence have shown that Convolutional Neural Networks can be excellent at extracting and identifying features in an image. However, Convolutional Neural Networks are often run on Expensive GPU’s with colossal memory and typically run millions of operations in a second. This is a challenging problem for embedded characterized by limited memory or processing power and a real-time capability. In this paper, a lightweight, robust and low memory architecture is explored to enable its incorporation as an embedded system. The proposed final architecture utilizes a novel semantic regression technique that integrates the accuracy of semantic segregation and the speed of regression. An end-to-end Deep learning system is used which takes images as an inputs and outputs the found lane in one shot. The developed system achieves 91.83% accuracy on Malaysian roads.

Item Type: Conference or Workshop Item (Plenary Papers)
Additional Information: 6796/79642
Uncontrolled Keywords: Lane detection; Advanced driver assistance system; Deep learning
Subjects: T Technology > T Technology (General)
T Technology > TK Electrical engineering. Electronics Nuclear engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Dr. Rashidah Funke Olanrewaju
Date Deposited: 23 Mar 2020 18:52
Last Modified: 15 Jul 2020 11:26
URI: http://irep.iium.edu.my/id/eprint/79642

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