Robust , fast and accurate lane departure warning system using deep learning and mobilenets
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...
| Main Authors: | , , , |
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| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/79642/ http://irep.iium.edu.my/79642/1/79642_Robust%2C%20Fast%20and%20Accurate%20Lane%20Departure%20_complete.pdf http://irep.iium.edu.my/79642/2/79642_Robust%2C%20Fast%20and%20Accurate%20Lane%20Departure%20_scopus.pdf |
| _version_ | 1848788806979813376 |
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| author | Olanrewaju, Rashidah Funke Ahmad Fakhri, Ahmad Syarifuddin Sanni, Mistura L. Ajala, Mosud Taiwo |
| author_facet | Olanrewaju, Rashidah Funke Ahmad Fakhri, Ahmad Syarifuddin Sanni, Mistura L. Ajala, Mosud Taiwo |
| author_sort | Olanrewaju, Rashidah Funke |
| building | IIUM Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T17:46:41Z |
| format | Proceeding Paper |
| id | iium-79642 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:46:41Z |
| publishDate | 2019 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-796422020-07-15T03:26:01Z http://irep.iium.edu.my/79642/ Robust , fast and accurate lane departure warning system using deep learning and mobilenets Olanrewaju, Rashidah Funke Ahmad Fakhri, Ahmad Syarifuddin Sanni, Mistura L. Ajala, Mosud Taiwo T Technology (General) TK Electrical engineering. Electronics Nuclear engineering 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. 2019-10 Proceeding Paper NonPeerReviewed application/pdf en http://irep.iium.edu.my/79642/1/79642_Robust%2C%20Fast%20and%20Accurate%20Lane%20Departure%20_complete.pdf application/pdf en http://irep.iium.edu.my/79642/2/79642_Robust%2C%20Fast%20and%20Accurate%20Lane%20Departure%20_scopus.pdf 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. https://ieeexplore-ieee-org.ezproxy.um.edu.my/document/8952067 |
| spellingShingle | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering Olanrewaju, Rashidah Funke Ahmad Fakhri, Ahmad Syarifuddin Sanni, Mistura L. Ajala, Mosud Taiwo Robust , fast and accurate lane departure warning system using deep learning and mobilenets |
| title | Robust , fast and accurate lane departure warning system using deep learning and mobilenets |
| title_full | Robust , fast and accurate lane departure warning system using deep learning and mobilenets |
| title_fullStr | Robust , fast and accurate lane departure warning system using deep learning and mobilenets |
| title_full_unstemmed | Robust , fast and accurate lane departure warning system using deep learning and mobilenets |
| title_short | Robust , fast and accurate lane departure warning system using deep learning and mobilenets |
| title_sort | robust , fast and accurate lane departure warning system using deep learning and mobilenets |
| topic | T Technology (General) TK Electrical engineering. Electronics Nuclear engineering |
| url | http://irep.iium.edu.my/79642/ http://irep.iium.edu.my/79642/ http://irep.iium.edu.my/79642/1/79642_Robust%2C%20Fast%20and%20Accurate%20Lane%20Departure%20_complete.pdf http://irep.iium.edu.my/79642/2/79642_Robust%2C%20Fast%20and%20Accurate%20Lane%20Departure%20_scopus.pdf |