Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review
Recently, the development and application of lane line departure warning systems have been in the market. For any of the systems, the key part of lane line tracking, lane line identification, or lane line departure warning is whether it can accurately and quickly detect lane lines. Since 1990s, they...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2020-12-01
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| Series: | Journal of Traffic and Transportation Engineering (English ed. Online) |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095756420301458 |
| _version_ | 1818650522535591936 |
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| author | Weiwei Chen Weixing Wang Kevin Wang Zhaoying Li Huan Li Sheng Liu |
| author_facet | Weiwei Chen Weixing Wang Kevin Wang Zhaoying Li Huan Li Sheng Liu |
| author_sort | Weiwei Chen |
| collection | DOAJ |
| description | Recently, the development and application of lane line departure warning systems have been in the market. For any of the systems, the key part of lane line tracking, lane line identification, or lane line departure warning is whether it can accurately and quickly detect lane lines. Since 1990s, they have been studied and implemented for the situations defined by the good viewing conditions and the clear lane markings on road. After then, the accuracy for particular situations, the robustness for a wide range of scenarios, time efficiency and integration into higher-order tasks define visual lane line detection and tracking as a continuing research subject. At present, these kinds of lane marking line detection methods based on machine vision and image processing can be divided into two categories: the traditional image processing and semantic segmentation (includes deep learning) methods. The former mainly involves feature-based and model-based steps, and which can be classified into similarity- and discontinuity-based ones; and the model-based step includes different parametric straight line, curve or pattern models. The semantic segmentation includes different machine learning, neural network and deep learning methods, which is the new trend for the research and application of lane line departure warning systems. This paper describes and analyzes the lane line departure warning systems, image processing algorithms and semantic segmentation methods for lane line detection. |
| first_indexed | 2024-12-17T01:51:33Z |
| format | Article |
| id | doaj.art-7e604491be644f7f91018c26da9239c6 |
| institution | Directory Open Access Journal |
| issn | 2095-7564 |
| language | English |
| last_indexed | 2024-12-17T01:51:33Z |
| publishDate | 2020-12-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Traffic and Transportation Engineering (English ed. Online) |
| spelling | doaj.art-7e604491be644f7f91018c26da9239c62022-12-21T22:08:03ZengKeAi Communications Co., Ltd.Journal of Traffic and Transportation Engineering (English ed. Online)2095-75642020-12-0176748774Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A reviewWeiwei Chen0Weixing Wang1Kevin Wang2Zhaoying Li3Huan Li4Sheng Liu5School of Information Engineering, Chang'an University, Xi'an 710064, China; Xi'an Aeronautical Polytechnic Institute, Xi'an 710089, ChinaSchool of Information Engineering, Chang'an University, Xi'an 710064, China; Royal Institute of Technology, Stockholm 10044, Sweden; Corresponding author. School of Information Engineering, Chang'an University, Xi'an 710064, China. Tel.: +86 29 8233 4562.Royal Institute of Technology, Stockholm 10044, SwedenAudible Inc., Newark, NJ 07102, USASchool of Information Engineering, Chang'an University, Xi'an 710064, ChinaSchool of Computer Science and Engineering, Xi'an University of Technology, Xi'an 710048, ChinaRecently, the development and application of lane line departure warning systems have been in the market. For any of the systems, the key part of lane line tracking, lane line identification, or lane line departure warning is whether it can accurately and quickly detect lane lines. Since 1990s, they have been studied and implemented for the situations defined by the good viewing conditions and the clear lane markings on road. After then, the accuracy for particular situations, the robustness for a wide range of scenarios, time efficiency and integration into higher-order tasks define visual lane line detection and tracking as a continuing research subject. At present, these kinds of lane marking line detection methods based on machine vision and image processing can be divided into two categories: the traditional image processing and semantic segmentation (includes deep learning) methods. The former mainly involves feature-based and model-based steps, and which can be classified into similarity- and discontinuity-based ones; and the model-based step includes different parametric straight line, curve or pattern models. The semantic segmentation includes different machine learning, neural network and deep learning methods, which is the new trend for the research and application of lane line departure warning systems. This paper describes and analyzes the lane line departure warning systems, image processing algorithms and semantic segmentation methods for lane line detection.http://www.sciencedirect.com/science/article/pii/S2095756420301458Traffic engineeringLane departure warningLane line detectionImage processingImage analysisSemantic segmentation |
| spellingShingle | Weiwei Chen Weixing Wang Kevin Wang Zhaoying Li Huan Li Sheng Liu Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review Journal of Traffic and Transportation Engineering (English ed. Online) Traffic engineering Lane departure warning Lane line detection Image processing Image analysis Semantic segmentation |
| title | Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review |
| title_full | Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review |
| title_fullStr | Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review |
| title_full_unstemmed | Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review |
| title_short | Lane departure warning systems and lane line detection methods based on image processing and semantic segmentation: A review |
| title_sort | lane departure warning systems and lane line detection methods based on image processing and semantic segmentation a review |
| topic | Traffic engineering Lane departure warning Lane line detection Image processing Image analysis Semantic segmentation |
| url | http://www.sciencedirect.com/science/article/pii/S2095756420301458 |
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