ST-MAE: robust lane detection in continuous multi-frame driving scenes based on a deep hybrid network Article Swipe
Rongyun Zhang
,
Yufeng Du
,
Peicheng Shi
,
Lifeng Zhao
,
Yaming Liu
,
Haoran Li
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s40747-022-00909-0
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1007/s40747-022-00909-0
Lane detection is one of the key techniques to realize advanced driving assistance and automatic driving. However, lane detection networks based on deep learning have significant shortcomings. The detection results are often unsatisfactory when there are shadows, degraded lane markings, and vehicle occlusion lanes. Therefore, a continuous multi-frame image sequence lane detection network is proposed. Specifically, the continuous six-frame image sequence is input into the network, in which the scene information of each frame image is extracted by an encoder composed of Swin Transformer blocks and input into the PredRNN. Continuous multi-frame of the driving scene is modeled as time-series by ST-LSTM blocks, and then, the shape changes and motion trajectory in the spatiotemporal sequence are effectively modeled. Finally, through the decoder composed of Swin Transformer blocks, the features are obtained and reconstructed to complete the detection task. Extensive experiments on two large-scale datasets demonstrate that the proposed method outperforms the competing methods in lane detection, especially in handling difficult situations. Experiments are carried out based on the TuSimple dataset. The results show: for easy scenes, the validation accuracy is 97.46%, the test accuracy is 97.37%, and the precision is 0.865. For complex scenes, the validation accuracy is 97.38%, the test accuracy is 97.29%, and the precision is 0.859. The running time is 4.4 ms. Experiments are carried out based on the CULane dataset. The results show that, for easy scenes, the validation accuracy is 97.03%, the test accuracy is 96.84%, and the precision is 0.837. For complex scenes, the validation accuracy is 96.18%, the test accuracy is 95.92%, and the precision is 0.829. The running time is 6.5 ms.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s40747-022-00909-0
- https://link.springer.com/content/pdf/10.1007/s40747-022-00909-0.pdf
- OA Status
- gold
- Cited By
- 3
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4309922936
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4309922936Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s40747-022-00909-0Digital Object Identifier
- Title
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ST-MAE: robust lane detection in continuous multi-frame driving scenes based on a deep hybrid networkWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-11-24Full publication date if available
- Authors
-
Rongyun Zhang, Yufeng Du, Peicheng Shi, Lifeng Zhao, Yaming Liu, Haoran LiList of authors in order
- Landing page
-
https://doi.org/10.1007/s40747-022-00909-0Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s40747-022-00909-0.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s40747-022-00909-0.pdfDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, Frame (networking), Computer vision, Encoder, Transformer, Pattern recognition (psychology), Engineering, Voltage, Telecommunications, Operating system, Electrical engineeringTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.of | 5, 72, 82, 93, 124 |
| abstract_inverted_index.on | 22, 141, 167, 221 |
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| abstract_inverted_index.4.4 | 214 |
| abstract_inverted_index.6.5 | 269 |
| abstract_inverted_index.For | 192, 247 |
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| abstract_inverted_index.and | 14, 41, 86, 104, 109, 132, 187, 205, 242, 260 |
| abstract_inverted_index.are | 31, 36, 116, 130, 163, 217 |
| abstract_inverted_index.for | 174, 229 |
| abstract_inverted_index.key | 7 |
| abstract_inverted_index.ms. | 215, 270 |
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| abstract_inverted_index.out | 165, 219 |
| abstract_inverted_index.the | 6, 57, 65, 69, 89, 94, 106, 113, 121, 128, 136, 147, 151, 168, 177, 182, 188, 195, 200, 206, 222, 232, 237, 243, 250, 255, 261 |
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| abstract_inverted_index.Swin | 83, 125 |
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| abstract_inverted_index.easy | 175, 230 |
| abstract_inverted_index.have | 25 |
| abstract_inverted_index.into | 64, 88 |
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| abstract_inverted_index.that | 146 |
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| abstract_inverted_index.often | 32 |
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| abstract_inverted_index.show: | 173 |
| abstract_inverted_index.task. | 138 |
| abstract_inverted_index.that, | 228 |
| abstract_inverted_index.then, | 105 |
| abstract_inverted_index.there | 35 |
| abstract_inverted_index.which | 68 |
| abstract_inverted_index.0.829. | 264 |
| abstract_inverted_index.0.837. | 246 |
| abstract_inverted_index.0.859. | 209 |
| abstract_inverted_index.0.865. | 191 |
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| abstract_inverted_index.lanes. | 44 |
| abstract_inverted_index.method | 149 |
| abstract_inverted_index.motion | 110 |
| abstract_inverted_index.95.92%, | 259 |
| abstract_inverted_index.96.18%, | 254 |
| abstract_inverted_index.96.84%, | 241 |
| abstract_inverted_index.97.03%, | 236 |
| abstract_inverted_index.97.29%, | 204 |
| abstract_inverted_index.97.37%, | 186 |
| abstract_inverted_index.97.38%, | 199 |
| abstract_inverted_index.97.46%, | 181 |
| abstract_inverted_index.ST-LSTM | 102 |
| abstract_inverted_index.blocks, | 103, 127 |
| abstract_inverted_index.carried | 164, 218 |
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| abstract_inverted_index.composed | 81, 123 |
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| abstract_inverted_index.extracted | 77 |
| abstract_inverted_index.markings, | 40 |
| abstract_inverted_index.occlusion | 43 |
| abstract_inverted_index.precision | 189, 207, 244, 262 |
| abstract_inverted_index.proposed. | 55 |
| abstract_inverted_index.six-frame | 59 |
| abstract_inverted_index.Continuous | 91 |
| abstract_inverted_index.Therefore, | 45 |
| abstract_inverted_index.assistance | 13 |
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| abstract_inverted_index.experiments | 140 |
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| abstract_inverted_index.situations. | 161 |
| abstract_inverted_index.time-series | 100 |
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| abstract_inverted_index.shortcomings. | 27 |
| abstract_inverted_index.spatiotemporal | 114 |
| abstract_inverted_index.unsatisfactory | 33 |
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