Real-Time Daytime Road Marker Recognition Using Features Vectors And Neural Network
Road markers provide vital information to ensure traffic safety. Different sets of markers are normally used between the highways and the normal road. At the normal road for example, the double lane markers are used to indicate the hazardous area, where overtaking is prohibited while broken marker...
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| Format: | Article |
| Language: | en |
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Institute Of Electrical And Electronics Engineers Inc. (IEEE)
2016
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| Online Access: | http://eprints.utem.edu.my/id/eprint/17270/1/Real-Time%20Daytime%20Road%20Marker%20Recognition%20Using%20Features%20Vectors%20And%20Neural%20Network.pdf http://eprints.utem.edu.my/id/eprint/17270/ http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7446223 |
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| author | Zamani, Md Sani Hadhrami, Abd Ghani Loi, Wei Sen Rosli, Besar |
| author_facet | Zamani, Md Sani Hadhrami, Abd Ghani Loi, Wei Sen Rosli, Besar |
| author_sort | Zamani, Md Sani |
| building | UTEM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Road markers provide vital information to ensure traffic safety. Different sets of markers are normally used between the highways and the normal road. At the normal road for example, the double lane markers are used to indicate the
hazardous area, where overtaking is prohibited while broken
marker lane indicate otherwise. To avoid traffic accidents and provide safety, these markers should be accurately detected and classified, which is best solved via vision detection approach. Marker type classification is however affected by the changing
sun illumination throughout the day. In this paper, real-time
recognition of these markers is developed using the artificial
neural network (ANN) to alert the users while driving. The
accuracy of the scheme is observed when different input features
(geometrical and texture) and image pixels are fed for
recognizing broken and double lane markers. A very high
accuracy result with low error rate is obtained at 98.83% (10-fold
cross validation) accuracy detection using additional features,
compared with ~95% by using only the image pixels as the input
vector and average processing time is at ~30ms per frame. |
| format | Article |
| id | my.utem.eprints-17270 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2016 |
| publisher | Institute Of Electrical And Electronics Engineers Inc. (IEEE) |
| record_format | eprints |
| spelling | my.utem.eprints-172702021-09-12T19:36:40Z http://eprints.utem.edu.my/id/eprint/17270/ Real-Time Daytime Road Marker Recognition Using Features Vectors And Neural Network Zamani, Md Sani Hadhrami, Abd Ghani Loi, Wei Sen Rosli, Besar T Technology (General) Road markers provide vital information to ensure traffic safety. Different sets of markers are normally used between the highways and the normal road. At the normal road for example, the double lane markers are used to indicate the hazardous area, where overtaking is prohibited while broken marker lane indicate otherwise. To avoid traffic accidents and provide safety, these markers should be accurately detected and classified, which is best solved via vision detection approach. Marker type classification is however affected by the changing sun illumination throughout the day. In this paper, real-time recognition of these markers is developed using the artificial neural network (ANN) to alert the users while driving. The accuracy of the scheme is observed when different input features (geometrical and texture) and image pixels are fed for recognizing broken and double lane markers. A very high accuracy result with low error rate is obtained at 98.83% (10-fold cross validation) accuracy detection using additional features, compared with ~95% by using only the image pixels as the input vector and average processing time is at ~30ms per frame. Institute Of Electrical And Electronics Engineers Inc. (IEEE) 2016 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/17270/1/Real-Time%20Daytime%20Road%20Marker%20Recognition%20Using%20Features%20Vectors%20And%20Neural%20Network.pdf Zamani, Md Sani and Hadhrami, Abd Ghani and Loi, Wei Sen and Rosli, Besar (2016) Real-Time Daytime Road Marker Recognition Using Features Vectors And Neural Network. 2015 IEEE Conference On Sustainable Utilization And Development In Engineering And Technology (CSUDET). pp. 38-43. ISSN 978-147998612-5 http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7446223 10.1109/CSUDET.2015.7446223 |
| spellingShingle | T Technology (General) Zamani, Md Sani Hadhrami, Abd Ghani Loi, Wei Sen Rosli, Besar Real-Time Daytime Road Marker Recognition Using Features Vectors And Neural Network |
| title | Real-Time Daytime Road Marker Recognition Using Features Vectors And Neural Network |
| title_full | Real-Time Daytime Road Marker Recognition Using Features Vectors And Neural Network |
| title_fullStr | Real-Time Daytime Road Marker Recognition Using Features Vectors And Neural Network |
| title_full_unstemmed | Real-Time Daytime Road Marker Recognition Using Features Vectors And Neural Network |
| title_short | Real-Time Daytime Road Marker Recognition Using Features Vectors And Neural Network |
| title_sort | real-time daytime road marker recognition using features vectors and neural network |
| topic | T Technology (General) |
| url | http://eprints.utem.edu.my/id/eprint/17270/1/Real-Time%20Daytime%20Road%20Marker%20Recognition%20Using%20Features%20Vectors%20And%20Neural%20Network.pdf http://eprints.utem.edu.my/id/eprint/17270/ http://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=7446223 |
| url_provider | http://eprints.utem.edu.my/ |