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== Abstract == | == Abstract == | ||
| − | Lane marking detection is part of most advanced driver assistance systems (ADAS) as an important component of computer vision. This paper presents a lane detection system based on a novel lane feature extraction approach. The robustness and real-time of algorithm enable different configurations of embedded solutions. The system is divided into three phases. Firstly, using the Prewitt operator we can get the rich useful details and using Shen Jun operator we can get step edge, on the other hand Shen Jun operator is the best filter to detect the symmetrical markings according to the maximum signal noise ratio (SNR) criterion. So we introduce the best compromise method between noise smoothing and edge locating that combining the Prewitt operator with Shen Jun operator to extract lane markings. Then a fast Hough transform based on image pyramid is applied to get the lane lines. The posterior algorithm of reasonably refining the Lane lines angle is introduced to correct to error caused by Hough transform. Finally, robust detection of | + | Lane marking detection is part of most advanced driver assistance systems (ADAS) as an important component of computer vision. This paper presents a lane detection system based on a novel lane feature extraction approach. The robustness and real-time of algorithm enable different configurations of embedded solutions. The system is divided into three phases. Firstly, using the Prewitt operator we can get the rich useful details and using Shen Jun operator we can get step edge, on the other hand Shen Jun operator is the best filter to detect the symmetrical markings according to the maximum signal noise ratio (SNR) criterion. So we introduce the best compromise method between noise smoothing and edge locating that combining the Prewitt operator with Shen Jun operator to extract lane markings. Then a fast Hough transform based on image pyramid is applied to get the lane lines. The posterior algorithm of reasonably refining the Lane lines angle is introduced to correct to error caused by Hough transform. Finally, robust detection of vehicleâs departure warning is also discussed. Experiment results on real road will be presented to prove the robustness and effectiveness of the proposed lane detection algorithm. |
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* [https://link.springer.com/content/pdf/10.1007%2F978-3-319-21969-1_20.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-319-21969-1_20.pdf] | * [https://link.springer.com/content/pdf/10.1007%2F978-3-319-21969-1_20.pdf https://link.springer.com/content/pdf/10.1007%2F978-3-319-21969-1_20.pdf] | ||
| − | * [http://link.springer.com/content/pdf/10.1007/978-3-319-21969-1_20 http://link.springer.com/content/pdf/10.1007/978-3-319-21969-1_20],[http://dx.doi.org/10.1007/978-3-319-21969-1_20 http://dx.doi.org/10.1007/978-3-319-21969-1_20] under the license http://www.springer.com/tdm | + | * [http://link.springer.com/content/pdf/10.1007/978-3-319-21969-1_20 http://link.springer.com/content/pdf/10.1007/978-3-319-21969-1_20], |
| + | : [http://dx.doi.org/10.1007/978-3-319-21969-1_20 http://dx.doi.org/10.1007/978-3-319-21969-1_20] under the license http://www.springer.com/tdm | ||
| − | * [https://link.springer.com/chapter/10.1007%2F978-3-319-21969-1_20 https://link.springer.com/chapter/10.1007%2F978-3-319-21969-1_20],[ | + | * [https://link.springer.com/chapter/10.1007%2F978-3-319-21969-1_20 https://link.springer.com/chapter/10.1007%2F978-3-319-21969-1_20], |
| + | : [https://dblp.uni-trier.de/db/conf/icig/icig2015-3.html#GuoZLLZ15 https://dblp.uni-trier.de/db/conf/icig/icig2015-3.html#GuoZLLZ15], | ||
| + | : [https://core.ac.uk/display/155696713 https://core.ac.uk/display/155696713], | ||
| + | : [https://academic.microsoft.com/#/detail/2287601897 https://academic.microsoft.com/#/detail/2287601897] | ||
Latest revision as of 17:03, 21 January 2021
Abstract
Lane marking detection is part of most advanced driver assistance systems (ADAS) as an important component of computer vision. This paper presents a lane detection system based on a novel lane feature extraction approach. The robustness and real-time of algorithm enable different configurations of embedded solutions. The system is divided into three phases. Firstly, using the Prewitt operator we can get the rich useful details and using Shen Jun operator we can get step edge, on the other hand Shen Jun operator is the best filter to detect the symmetrical markings according to the maximum signal noise ratio (SNR) criterion. So we introduce the best compromise method between noise smoothing and edge locating that combining the Prewitt operator with Shen Jun operator to extract lane markings. Then a fast Hough transform based on image pyramid is applied to get the lane lines. The posterior algorithm of reasonably refining the Lane lines angle is introduced to correct to error caused by Hough transform. Finally, robust detection of vehicleâs departure warning is also discussed. Experiment results on real road will be presented to prove the robustness and effectiveness of the proposed lane detection algorithm.
Original document
The different versions of the original document can be found in:
- http://dx.doi.org/10.1007/978-3-319-21969-1_20 under the license http://www.springer.com/tdm
Document information
Published on 01/01/2015
Volume 2015, 2015
DOI: 10.1007/978-3-319-21969-1_20
Licence: CC BY-NC-SA license
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