ST-LaneNet: Lane Line Detection Method Based on Swin Transformer and LaneNet Article Swipe
Yufeng Du
,
Rongyun Zhang
,
Peicheng Shi
,
Linfeng Zhao
,
Bin Zhang
,
Yaming Liu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s10033-024-00992-z
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s10033-024-00992-z
The advancement of autonomous driving heavily relies on the ability to accurate lane lines detection. As deep learning and computer vision technologies evolve, a variety of deep learning-based methods for lane line detection have been proposed by researchers in the field. However, owing to the simple appearance of lane lines and the lack of distinctive features, it is easy for other objects with similar local appearances to interfere with the process of detecting lane lines. The precision of lane line detection is limited by the unpredictable quantity and diversity of lane lines. To address the aforementioned challenges, we propose a novel deep learning approach for lane line detection. This method leverages the Swin Transformer in conjunction with LaneNet (called ST-LaneNet). The experience results showed that the true positive detection rate can reach 97.53% for easy lanes and 96.83% for difficult lanes (such as scenes with severe occlusion and extreme lighting conditions), which can better accomplish the objective of detecting lane lines. In 1000 detection samples, the average detection accuracy can reach 97.83%, the average inference time per image can reach 17.8 ms, and the average number of frames per second can reach 64.8 Hz. The programming scripts and associated models for this project can be accessed openly at the following GitHub repository: https://github.com/Duane711/Lane-line-detection-ST-LaneNet .
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s10033-024-00992-z
- https://cjme.springeropen.com/counter/pdf/10.1186/s10033-024-00992-z
- OA Status
- diamond
- Cited By
- 7
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4392166254
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- OpenAlex ID
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https://openalex.org/W4392166254Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1186/s10033-024-00992-zDigital Object Identifier
- Title
-
ST-LaneNet: Lane Line Detection Method Based on Swin Transformer and LaneNetWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-02-26Full publication date if available
- Authors
-
Yufeng Du, Rongyun Zhang, Peicheng Shi, Linfeng Zhao, Bin Zhang, Yaming LiuList of authors in order
- Landing page
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https://doi.org/10.1186/s10033-024-00992-zPublisher landing page
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https://cjme.springeropen.com/counter/pdf/10.1186/s10033-024-00992-zDirect link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://cjme.springeropen.com/counter/pdf/10.1186/s10033-024-00992-zDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, Computer vision, Line (geometry), Source lines of code, Transformer, Deep learning, Scripting language, Inference, Voltage, Software, Engineering, Mathematics, Geometry, Programming language, Electrical engineering, Operating systemTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 4, 2024: 3Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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