Lane line detection based on the codec structure of the attention mechanism
Abstract For self-driving cars and advanced driver assistance systems, lane detection is imperative. On the one hand, numerous current lane line detection algorithms perform dense pixel-by-pixel prediction followed by complex post-processing. On the other hand, as lane lines only account for a small...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Zhao, Qinghua [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of real-time image processing - Berlin : Springer, 2006, 19(2022), 4 vom: 09. Mai, Seite 715-726 |
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| Übergeordnetes Werk: |
volume:19 ; year:2022 ; number:4 ; day:09 ; month:05 ; pages:715-726 |
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| DOI / URN: |
10.1007/s11554-022-01217-z |
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| Katalog-ID: |
SPR047462388 |
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| 100 | 1 | |a Zhao, Qinghua |e verfasserin |4 aut | |
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| 520 | |a Abstract For self-driving cars and advanced driver assistance systems, lane detection is imperative. On the one hand, numerous current lane line detection algorithms perform dense pixel-by-pixel prediction followed by complex post-processing. On the other hand, as lane lines only account for a small part of the whole image, there are only very subtle and sparse signals, and information is lost during long-distance transmission. Therefore, it is difficult for an ordinary convolutional neural network to resolve challenging scenes, such as severe occlusion, congested roads, and poor lighting conditions. To address these issues, in this study, we propose an encoder–decoder architecture based on an attention mechanism. The encoder module is employed to initially extract the lane line features. We propose a spatial recurrent feature-shift aggregator module to further enrich the lane line features, which transmits information from four directions (up, down, left, and right). In addition, this module contains the spatial attention feature that focuses on useful information for lane line detection and reduces redundant computations. In particular, to reduce the occurrence of incorrect predictions and the need for post-processing, we add channel attention between the encoding and decoding. It processes encoding and decoding to obtain multidimensional attention information, respectively. Our method achieved novel results on two popular lane detection benchmarks (CULane F1-measure 76.2, TuSimple accuracy 96.85%), which can reach 48 frames per second and meet the real-time requirements of autonomous driving. | ||
| 650 | 4 | |a Lane detection |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Autonomous driving |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Channel attention |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Spatial attention |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Peng, Qi |4 aut | |
| 700 | 1 | |a Zhuang, Yiqi |4 aut | |
| 773 | 0 | 8 | |i Enthalten in |t Journal of real-time image processing |d Berlin : Springer, 2006 |g 19(2022), 4 vom: 09. Mai, Seite 715-726 |w (DE-627)52836118X |w (DE-600)2280192-3 |x 1861-8219 |7 nnns |
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10.1007/s11554-022-01217-z doi (DE-627)SPR047462388 (SPR)s11554-022-01217-z-e DE-627 ger DE-627 rakwb eng Zhao, Qinghua verfasserin aut Lane line detection based on the codec structure of the attention mechanism 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract For self-driving cars and advanced driver assistance systems, lane detection is imperative. On the one hand, numerous current lane line detection algorithms perform dense pixel-by-pixel prediction followed by complex post-processing. On the other hand, as lane lines only account for a small part of the whole image, there are only very subtle and sparse signals, and information is lost during long-distance transmission. Therefore, it is difficult for an ordinary convolutional neural network to resolve challenging scenes, such as severe occlusion, congested roads, and poor lighting conditions. To address these issues, in this study, we propose an encoder–decoder architecture based on an attention mechanism. The encoder module is employed to initially extract the lane line features. We propose a spatial recurrent feature-shift aggregator module to further enrich the lane line features, which transmits information from four directions (up, down, left, and right). In addition, this module contains the spatial attention feature that focuses on useful information for lane line detection and reduces redundant computations. In particular, to reduce the occurrence of incorrect predictions and the need for post-processing, we add channel attention between the encoding and decoding. It processes encoding and decoding to obtain multidimensional attention information, respectively. Our method achieved novel results on two popular lane detection benchmarks (CULane F1-measure 76.2, TuSimple accuracy 96.85%), which can reach 48 frames per second and meet the real-time requirements of autonomous driving. Lane detection (dpeaa)DE-He213 Autonomous driving (dpeaa)DE-He213 Channel attention (dpeaa)DE-He213 Spatial attention (dpeaa)DE-He213 Peng, Qi aut Zhuang, Yiqi aut Enthalten in Journal of real-time image processing Berlin : Springer, 2006 19(2022), 4 vom: 09. Mai, Seite 715-726 (DE-627)52836118X (DE-600)2280192-3 1861-8219 nnns volume:19 year:2022 number:4 day:09 month:05 pages:715-726 https://dx.doi.org/10.1007/s11554-022-01217-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2022 4 09 05 715-726 |
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10.1007/s11554-022-01217-z doi (DE-627)SPR047462388 (SPR)s11554-022-01217-z-e DE-627 ger DE-627 rakwb eng Zhao, Qinghua verfasserin aut Lane line detection based on the codec structure of the attention mechanism 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract For self-driving cars and advanced driver assistance systems, lane detection is imperative. On the one hand, numerous current lane line detection algorithms perform dense pixel-by-pixel prediction followed by complex post-processing. On the other hand, as lane lines only account for a small part of the whole image, there are only very subtle and sparse signals, and information is lost during long-distance transmission. Therefore, it is difficult for an ordinary convolutional neural network to resolve challenging scenes, such as severe occlusion, congested roads, and poor lighting conditions. To address these issues, in this study, we propose an encoder–decoder architecture based on an attention mechanism. The encoder module is employed to initially extract the lane line features. We propose a spatial recurrent feature-shift aggregator module to further enrich the lane line features, which transmits information from four directions (up, down, left, and right). In addition, this module contains the spatial attention feature that focuses on useful information for lane line detection and reduces redundant computations. In particular, to reduce the occurrence of incorrect predictions and the need for post-processing, we add channel attention between the encoding and decoding. It processes encoding and decoding to obtain multidimensional attention information, respectively. Our method achieved novel results on two popular lane detection benchmarks (CULane F1-measure 76.2, TuSimple accuracy 96.85%), which can reach 48 frames per second and meet the real-time requirements of autonomous driving. Lane detection (dpeaa)DE-He213 Autonomous driving (dpeaa)DE-He213 Channel attention (dpeaa)DE-He213 Spatial attention (dpeaa)DE-He213 Peng, Qi aut Zhuang, Yiqi aut Enthalten in Journal of real-time image processing Berlin : Springer, 2006 19(2022), 4 vom: 09. Mai, Seite 715-726 (DE-627)52836118X (DE-600)2280192-3 1861-8219 nnns volume:19 year:2022 number:4 day:09 month:05 pages:715-726 https://dx.doi.org/10.1007/s11554-022-01217-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2022 4 09 05 715-726 |
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10.1007/s11554-022-01217-z doi (DE-627)SPR047462388 (SPR)s11554-022-01217-z-e DE-627 ger DE-627 rakwb eng Zhao, Qinghua verfasserin aut Lane line detection based on the codec structure of the attention mechanism 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract For self-driving cars and advanced driver assistance systems, lane detection is imperative. On the one hand, numerous current lane line detection algorithms perform dense pixel-by-pixel prediction followed by complex post-processing. On the other hand, as lane lines only account for a small part of the whole image, there are only very subtle and sparse signals, and information is lost during long-distance transmission. Therefore, it is difficult for an ordinary convolutional neural network to resolve challenging scenes, such as severe occlusion, congested roads, and poor lighting conditions. To address these issues, in this study, we propose an encoder–decoder architecture based on an attention mechanism. The encoder module is employed to initially extract the lane line features. We propose a spatial recurrent feature-shift aggregator module to further enrich the lane line features, which transmits information from four directions (up, down, left, and right). In addition, this module contains the spatial attention feature that focuses on useful information for lane line detection and reduces redundant computations. In particular, to reduce the occurrence of incorrect predictions and the need for post-processing, we add channel attention between the encoding and decoding. It processes encoding and decoding to obtain multidimensional attention information, respectively. Our method achieved novel results on two popular lane detection benchmarks (CULane F1-measure 76.2, TuSimple accuracy 96.85%), which can reach 48 frames per second and meet the real-time requirements of autonomous driving. Lane detection (dpeaa)DE-He213 Autonomous driving (dpeaa)DE-He213 Channel attention (dpeaa)DE-He213 Spatial attention (dpeaa)DE-He213 Peng, Qi aut Zhuang, Yiqi aut Enthalten in Journal of real-time image processing Berlin : Springer, 2006 19(2022), 4 vom: 09. Mai, Seite 715-726 (DE-627)52836118X (DE-600)2280192-3 1861-8219 nnns volume:19 year:2022 number:4 day:09 month:05 pages:715-726 https://dx.doi.org/10.1007/s11554-022-01217-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2022 4 09 05 715-726 |
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10.1007/s11554-022-01217-z doi (DE-627)SPR047462388 (SPR)s11554-022-01217-z-e DE-627 ger DE-627 rakwb eng Zhao, Qinghua verfasserin aut Lane line detection based on the codec structure of the attention mechanism 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract For self-driving cars and advanced driver assistance systems, lane detection is imperative. On the one hand, numerous current lane line detection algorithms perform dense pixel-by-pixel prediction followed by complex post-processing. On the other hand, as lane lines only account for a small part of the whole image, there are only very subtle and sparse signals, and information is lost during long-distance transmission. Therefore, it is difficult for an ordinary convolutional neural network to resolve challenging scenes, such as severe occlusion, congested roads, and poor lighting conditions. To address these issues, in this study, we propose an encoder–decoder architecture based on an attention mechanism. The encoder module is employed to initially extract the lane line features. We propose a spatial recurrent feature-shift aggregator module to further enrich the lane line features, which transmits information from four directions (up, down, left, and right). In addition, this module contains the spatial attention feature that focuses on useful information for lane line detection and reduces redundant computations. In particular, to reduce the occurrence of incorrect predictions and the need for post-processing, we add channel attention between the encoding and decoding. It processes encoding and decoding to obtain multidimensional attention information, respectively. Our method achieved novel results on two popular lane detection benchmarks (CULane F1-measure 76.2, TuSimple accuracy 96.85%), which can reach 48 frames per second and meet the real-time requirements of autonomous driving. Lane detection (dpeaa)DE-He213 Autonomous driving (dpeaa)DE-He213 Channel attention (dpeaa)DE-He213 Spatial attention (dpeaa)DE-He213 Peng, Qi aut Zhuang, Yiqi aut Enthalten in Journal of real-time image processing Berlin : Springer, 2006 19(2022), 4 vom: 09. Mai, Seite 715-726 (DE-627)52836118X (DE-600)2280192-3 1861-8219 nnns volume:19 year:2022 number:4 day:09 month:05 pages:715-726 https://dx.doi.org/10.1007/s11554-022-01217-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2022 4 09 05 715-726 |
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10.1007/s11554-022-01217-z doi (DE-627)SPR047462388 (SPR)s11554-022-01217-z-e DE-627 ger DE-627 rakwb eng Zhao, Qinghua verfasserin aut Lane line detection based on the codec structure of the attention mechanism 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 Abstract For self-driving cars and advanced driver assistance systems, lane detection is imperative. On the one hand, numerous current lane line detection algorithms perform dense pixel-by-pixel prediction followed by complex post-processing. On the other hand, as lane lines only account for a small part of the whole image, there are only very subtle and sparse signals, and information is lost during long-distance transmission. Therefore, it is difficult for an ordinary convolutional neural network to resolve challenging scenes, such as severe occlusion, congested roads, and poor lighting conditions. To address these issues, in this study, we propose an encoder–decoder architecture based on an attention mechanism. The encoder module is employed to initially extract the lane line features. We propose a spatial recurrent feature-shift aggregator module to further enrich the lane line features, which transmits information from four directions (up, down, left, and right). In addition, this module contains the spatial attention feature that focuses on useful information for lane line detection and reduces redundant computations. In particular, to reduce the occurrence of incorrect predictions and the need for post-processing, we add channel attention between the encoding and decoding. It processes encoding and decoding to obtain multidimensional attention information, respectively. Our method achieved novel results on two popular lane detection benchmarks (CULane F1-measure 76.2, TuSimple accuracy 96.85%), which can reach 48 frames per second and meet the real-time requirements of autonomous driving. Lane detection (dpeaa)DE-He213 Autonomous driving (dpeaa)DE-He213 Channel attention (dpeaa)DE-He213 Spatial attention (dpeaa)DE-He213 Peng, Qi aut Zhuang, Yiqi aut Enthalten in Journal of real-time image processing Berlin : Springer, 2006 19(2022), 4 vom: 09. Mai, Seite 715-726 (DE-627)52836118X (DE-600)2280192-3 1861-8219 nnns volume:19 year:2022 number:4 day:09 month:05 pages:715-726 https://dx.doi.org/10.1007/s11554-022-01217-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_152 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4328 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 19 2022 4 09 05 715-726 |
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On the one hand, numerous current lane line detection algorithms perform dense pixel-by-pixel prediction followed by complex post-processing. On the other hand, as lane lines only account for a small part of the whole image, there are only very subtle and sparse signals, and information is lost during long-distance transmission. Therefore, it is difficult for an ordinary convolutional neural network to resolve challenging scenes, such as severe occlusion, congested roads, and poor lighting conditions. To address these issues, in this study, we propose an encoder–decoder architecture based on an attention mechanism. The encoder module is employed to initially extract the lane line features. We propose a spatial recurrent feature-shift aggregator module to further enrich the lane line features, which transmits information from four directions (up, down, left, and right). 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Zhao, Qinghua |
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Zhao, Qinghua misc Lane detection misc Autonomous driving misc Channel attention misc Spatial attention Lane line detection based on the codec structure of the attention mechanism |
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Lane line detection based on the codec structure of the attention mechanism Lane detection (dpeaa)DE-He213 Autonomous driving (dpeaa)DE-He213 Channel attention (dpeaa)DE-He213 Spatial attention (dpeaa)DE-He213 |
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lane line detection based on the codec structure of the attention mechanism |
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Lane line detection based on the codec structure of the attention mechanism |
| abstract |
Abstract For self-driving cars and advanced driver assistance systems, lane detection is imperative. On the one hand, numerous current lane line detection algorithms perform dense pixel-by-pixel prediction followed by complex post-processing. On the other hand, as lane lines only account for a small part of the whole image, there are only very subtle and sparse signals, and information is lost during long-distance transmission. Therefore, it is difficult for an ordinary convolutional neural network to resolve challenging scenes, such as severe occlusion, congested roads, and poor lighting conditions. To address these issues, in this study, we propose an encoder–decoder architecture based on an attention mechanism. The encoder module is employed to initially extract the lane line features. We propose a spatial recurrent feature-shift aggregator module to further enrich the lane line features, which transmits information from four directions (up, down, left, and right). In addition, this module contains the spatial attention feature that focuses on useful information for lane line detection and reduces redundant computations. In particular, to reduce the occurrence of incorrect predictions and the need for post-processing, we add channel attention between the encoding and decoding. It processes encoding and decoding to obtain multidimensional attention information, respectively. Our method achieved novel results on two popular lane detection benchmarks (CULane F1-measure 76.2, TuSimple accuracy 96.85%), which can reach 48 frames per second and meet the real-time requirements of autonomous driving. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstractGer |
Abstract For self-driving cars and advanced driver assistance systems, lane detection is imperative. On the one hand, numerous current lane line detection algorithms perform dense pixel-by-pixel prediction followed by complex post-processing. On the other hand, as lane lines only account for a small part of the whole image, there are only very subtle and sparse signals, and information is lost during long-distance transmission. Therefore, it is difficult for an ordinary convolutional neural network to resolve challenging scenes, such as severe occlusion, congested roads, and poor lighting conditions. To address these issues, in this study, we propose an encoder–decoder architecture based on an attention mechanism. The encoder module is employed to initially extract the lane line features. We propose a spatial recurrent feature-shift aggregator module to further enrich the lane line features, which transmits information from four directions (up, down, left, and right). In addition, this module contains the spatial attention feature that focuses on useful information for lane line detection and reduces redundant computations. In particular, to reduce the occurrence of incorrect predictions and the need for post-processing, we add channel attention between the encoding and decoding. It processes encoding and decoding to obtain multidimensional attention information, respectively. Our method achieved novel results on two popular lane detection benchmarks (CULane F1-measure 76.2, TuSimple accuracy 96.85%), which can reach 48 frames per second and meet the real-time requirements of autonomous driving. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
| abstract_unstemmed |
Abstract For self-driving cars and advanced driver assistance systems, lane detection is imperative. On the one hand, numerous current lane line detection algorithms perform dense pixel-by-pixel prediction followed by complex post-processing. On the other hand, as lane lines only account for a small part of the whole image, there are only very subtle and sparse signals, and information is lost during long-distance transmission. Therefore, it is difficult for an ordinary convolutional neural network to resolve challenging scenes, such as severe occlusion, congested roads, and poor lighting conditions. To address these issues, in this study, we propose an encoder–decoder architecture based on an attention mechanism. The encoder module is employed to initially extract the lane line features. We propose a spatial recurrent feature-shift aggregator module to further enrich the lane line features, which transmits information from four directions (up, down, left, and right). In addition, this module contains the spatial attention feature that focuses on useful information for lane line detection and reduces redundant computations. In particular, to reduce the occurrence of incorrect predictions and the need for post-processing, we add channel attention between the encoding and decoding. It processes encoding and decoding to obtain multidimensional attention information, respectively. Our method achieved novel results on two popular lane detection benchmarks (CULane F1-measure 76.2, TuSimple accuracy 96.85%), which can reach 48 frames per second and meet the real-time requirements of autonomous driving. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022 |
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Lane line detection based on the codec structure of the attention mechanism |
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https://dx.doi.org/10.1007/s11554-022-01217-z |
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Peng, Qi Zhuang, Yiqi |
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Peng, Qi Zhuang, Yiqi |
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| doi_str |
10.1007/s11554-022-01217-z |
| up_date |
2024-07-04T03:12:23.945Z |
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| score |
7.399886 |