A real-time detection method of multi-scale traffic signs based on dynamic pruning strategy
Abstract Traffic sign detection can provide important judgment information for the unmanned driving system. To deploy the detector on edge equipment and have better detection performance, a real-time detection method based on a dynamic pruning strategy is proposed. First of all, we propose to add a...
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| Autor*in: |
Jiang, Qunyan [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Anmerkung: |
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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| Übergeordnetes Werk: |
Enthalten in: Multimedia tools and applications - Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995, 82(2023), 21 vom: 22. März, Seite 32519-32537 |
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| Übergeordnetes Werk: |
volume:82 ; year:2023 ; number:21 ; day:22 ; month:03 ; pages:32519-32537 |
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| DOI / URN: |
10.1007/s11042-023-15109-2 |
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| Katalog-ID: |
SPR052940497 |
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| 520 | |a Abstract Traffic sign detection can provide important judgment information for the unmanned driving system. To deploy the detector on edge equipment and have better detection performance, a real-time detection method based on a dynamic pruning strategy is proposed. First of all, we propose to add a shallow prediction branch to the initial model, which makes the three-scale prediction branch become the four-scale prediction branch. The preliminary improved network expands the detection range and improves the detection accuracy of the model. Secondly, we propose a dynamic channel pruning method to prune the model, which can greatly reduce the amount of calculation without reducing the accuracy of the model, thus improving the speed of traffic sign detection. Thirdly, the weight sharing method is used to replace different weights in the same cluster with the same value, which reduces the occupied hardware resources and realizes rapid deployment of the model. Finally, to increase the number of rare traffic signs, we propose to merge the TT 100 K and CCTSDB into one dataset. The experimental results show that the improved model not only improves the detection accuracy, but also improves the inference time, which is conducive to the deployment of the model on resource-constrained devices. | ||
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| 650 | 4 | |a Traffic sign detection |7 (dpeaa)DE-He213 | |
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| 700 | 1 | |a Lu, Guanlin |4 aut | |
| 700 | 1 | |a Wang, Jinkang |4 aut | |
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10.1007/s11042-023-15109-2 doi (DE-627)SPR052940497 (SPR)s11042-023-15109-2-e DE-627 ger DE-627 rakwb eng Jiang, Qunyan verfasserin aut A real-time detection method of multi-scale traffic signs based on dynamic pruning strategy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traffic sign detection can provide important judgment information for the unmanned driving system. To deploy the detector on edge equipment and have better detection performance, a real-time detection method based on a dynamic pruning strategy is proposed. First of all, we propose to add a shallow prediction branch to the initial model, which makes the three-scale prediction branch become the four-scale prediction branch. The preliminary improved network expands the detection range and improves the detection accuracy of the model. Secondly, we propose a dynamic channel pruning method to prune the model, which can greatly reduce the amount of calculation without reducing the accuracy of the model, thus improving the speed of traffic sign detection. Thirdly, the weight sharing method is used to replace different weights in the same cluster with the same value, which reduces the occupied hardware resources and realizes rapid deployment of the model. Finally, to increase the number of rare traffic signs, we propose to merge the TT 100 K and CCTSDB into one dataset. The experimental results show that the improved model not only improves the detection accuracy, but also improves the inference time, which is conducive to the deployment of the model on resource-constrained devices. YOLOv5l (dpeaa)DE-He213 Dynamic pruning (dpeaa)DE-He213 Weight sharing (dpeaa)DE-He213 Traffic sign detection (dpeaa)DE-He213 Rui, Ting aut Dai, Juying aut Shao, Faming aut Lu, Guanlin aut Wang, Jinkang aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 21 vom: 22. März, Seite 32519-32537 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:21 day:22 month:03 pages:32519-32537 https://dx.doi.org/10.1007/s11042-023-15109-2 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_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_2119 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 82 2023 21 22 03 32519-32537 |
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10.1007/s11042-023-15109-2 doi (DE-627)SPR052940497 (SPR)s11042-023-15109-2-e DE-627 ger DE-627 rakwb eng Jiang, Qunyan verfasserin aut A real-time detection method of multi-scale traffic signs based on dynamic pruning strategy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traffic sign detection can provide important judgment information for the unmanned driving system. To deploy the detector on edge equipment and have better detection performance, a real-time detection method based on a dynamic pruning strategy is proposed. First of all, we propose to add a shallow prediction branch to the initial model, which makes the three-scale prediction branch become the four-scale prediction branch. The preliminary improved network expands the detection range and improves the detection accuracy of the model. Secondly, we propose a dynamic channel pruning method to prune the model, which can greatly reduce the amount of calculation without reducing the accuracy of the model, thus improving the speed of traffic sign detection. Thirdly, the weight sharing method is used to replace different weights in the same cluster with the same value, which reduces the occupied hardware resources and realizes rapid deployment of the model. Finally, to increase the number of rare traffic signs, we propose to merge the TT 100 K and CCTSDB into one dataset. The experimental results show that the improved model not only improves the detection accuracy, but also improves the inference time, which is conducive to the deployment of the model on resource-constrained devices. YOLOv5l (dpeaa)DE-He213 Dynamic pruning (dpeaa)DE-He213 Weight sharing (dpeaa)DE-He213 Traffic sign detection (dpeaa)DE-He213 Rui, Ting aut Dai, Juying aut Shao, Faming aut Lu, Guanlin aut Wang, Jinkang aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 21 vom: 22. März, Seite 32519-32537 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:21 day:22 month:03 pages:32519-32537 https://dx.doi.org/10.1007/s11042-023-15109-2 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_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_2119 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 82 2023 21 22 03 32519-32537 |
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10.1007/s11042-023-15109-2 doi (DE-627)SPR052940497 (SPR)s11042-023-15109-2-e DE-627 ger DE-627 rakwb eng Jiang, Qunyan verfasserin aut A real-time detection method of multi-scale traffic signs based on dynamic pruning strategy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traffic sign detection can provide important judgment information for the unmanned driving system. To deploy the detector on edge equipment and have better detection performance, a real-time detection method based on a dynamic pruning strategy is proposed. First of all, we propose to add a shallow prediction branch to the initial model, which makes the three-scale prediction branch become the four-scale prediction branch. The preliminary improved network expands the detection range and improves the detection accuracy of the model. Secondly, we propose a dynamic channel pruning method to prune the model, which can greatly reduce the amount of calculation without reducing the accuracy of the model, thus improving the speed of traffic sign detection. Thirdly, the weight sharing method is used to replace different weights in the same cluster with the same value, which reduces the occupied hardware resources and realizes rapid deployment of the model. Finally, to increase the number of rare traffic signs, we propose to merge the TT 100 K and CCTSDB into one dataset. The experimental results show that the improved model not only improves the detection accuracy, but also improves the inference time, which is conducive to the deployment of the model on resource-constrained devices. YOLOv5l (dpeaa)DE-He213 Dynamic pruning (dpeaa)DE-He213 Weight sharing (dpeaa)DE-He213 Traffic sign detection (dpeaa)DE-He213 Rui, Ting aut Dai, Juying aut Shao, Faming aut Lu, Guanlin aut Wang, Jinkang aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 21 vom: 22. März, Seite 32519-32537 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:21 day:22 month:03 pages:32519-32537 https://dx.doi.org/10.1007/s11042-023-15109-2 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_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_2119 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 82 2023 21 22 03 32519-32537 |
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10.1007/s11042-023-15109-2 doi (DE-627)SPR052940497 (SPR)s11042-023-15109-2-e DE-627 ger DE-627 rakwb eng Jiang, Qunyan verfasserin aut A real-time detection method of multi-scale traffic signs based on dynamic pruning strategy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traffic sign detection can provide important judgment information for the unmanned driving system. To deploy the detector on edge equipment and have better detection performance, a real-time detection method based on a dynamic pruning strategy is proposed. First of all, we propose to add a shallow prediction branch to the initial model, which makes the three-scale prediction branch become the four-scale prediction branch. The preliminary improved network expands the detection range and improves the detection accuracy of the model. Secondly, we propose a dynamic channel pruning method to prune the model, which can greatly reduce the amount of calculation without reducing the accuracy of the model, thus improving the speed of traffic sign detection. Thirdly, the weight sharing method is used to replace different weights in the same cluster with the same value, which reduces the occupied hardware resources and realizes rapid deployment of the model. Finally, to increase the number of rare traffic signs, we propose to merge the TT 100 K and CCTSDB into one dataset. The experimental results show that the improved model not only improves the detection accuracy, but also improves the inference time, which is conducive to the deployment of the model on resource-constrained devices. YOLOv5l (dpeaa)DE-He213 Dynamic pruning (dpeaa)DE-He213 Weight sharing (dpeaa)DE-He213 Traffic sign detection (dpeaa)DE-He213 Rui, Ting aut Dai, Juying aut Shao, Faming aut Lu, Guanlin aut Wang, Jinkang aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 21 vom: 22. März, Seite 32519-32537 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:21 day:22 month:03 pages:32519-32537 https://dx.doi.org/10.1007/s11042-023-15109-2 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_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_2119 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 82 2023 21 22 03 32519-32537 |
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10.1007/s11042-023-15109-2 doi (DE-627)SPR052940497 (SPR)s11042-023-15109-2-e DE-627 ger DE-627 rakwb eng Jiang, Qunyan verfasserin aut A real-time detection method of multi-scale traffic signs based on dynamic pruning strategy 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. Abstract Traffic sign detection can provide important judgment information for the unmanned driving system. To deploy the detector on edge equipment and have better detection performance, a real-time detection method based on a dynamic pruning strategy is proposed. First of all, we propose to add a shallow prediction branch to the initial model, which makes the three-scale prediction branch become the four-scale prediction branch. The preliminary improved network expands the detection range and improves the detection accuracy of the model. Secondly, we propose a dynamic channel pruning method to prune the model, which can greatly reduce the amount of calculation without reducing the accuracy of the model, thus improving the speed of traffic sign detection. Thirdly, the weight sharing method is used to replace different weights in the same cluster with the same value, which reduces the occupied hardware resources and realizes rapid deployment of the model. Finally, to increase the number of rare traffic signs, we propose to merge the TT 100 K and CCTSDB into one dataset. The experimental results show that the improved model not only improves the detection accuracy, but also improves the inference time, which is conducive to the deployment of the model on resource-constrained devices. YOLOv5l (dpeaa)DE-He213 Dynamic pruning (dpeaa)DE-He213 Weight sharing (dpeaa)DE-He213 Traffic sign detection (dpeaa)DE-He213 Rui, Ting aut Dai, Juying aut Shao, Faming aut Lu, Guanlin aut Wang, Jinkang aut Enthalten in Multimedia tools and applications Dordrecht [u.a.] : Springer Science + Business Media B.V, 1995 82(2023), 21 vom: 22. März, Seite 32519-32537 (DE-627)27135030X (DE-600)1479928-5 1573-7721 nnns volume:82 year:2023 number:21 day:22 month:03 pages:32519-32537 https://dx.doi.org/10.1007/s11042-023-15109-2 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_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_2119 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 82 2023 21 22 03 32519-32537 |
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Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract Traffic sign detection can provide important judgment information for the unmanned driving system. To deploy the detector on edge equipment and have better detection performance, a real-time detection method based on a dynamic pruning strategy is proposed. First of all, we propose to add a shallow prediction branch to the initial model, which makes the three-scale prediction branch become the four-scale prediction branch. The preliminary improved network expands the detection range and improves the detection accuracy of the model. 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Jiang, Qunyan |
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real-time detection method of multi-scale traffic signs based on dynamic pruning strategy |
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A real-time detection method of multi-scale traffic signs based on dynamic pruning strategy |
| abstract |
Abstract Traffic sign detection can provide important judgment information for the unmanned driving system. To deploy the detector on edge equipment and have better detection performance, a real-time detection method based on a dynamic pruning strategy is proposed. First of all, we propose to add a shallow prediction branch to the initial model, which makes the three-scale prediction branch become the four-scale prediction branch. The preliminary improved network expands the detection range and improves the detection accuracy of the model. Secondly, we propose a dynamic channel pruning method to prune the model, which can greatly reduce the amount of calculation without reducing the accuracy of the model, thus improving the speed of traffic sign detection. Thirdly, the weight sharing method is used to replace different weights in the same cluster with the same value, which reduces the occupied hardware resources and realizes rapid deployment of the model. Finally, to increase the number of rare traffic signs, we propose to merge the TT 100 K and CCTSDB into one dataset. The experimental results show that the improved model not only improves the detection accuracy, but also improves the inference time, which is conducive to the deployment of the model on resource-constrained devices. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstractGer |
Abstract Traffic sign detection can provide important judgment information for the unmanned driving system. To deploy the detector on edge equipment and have better detection performance, a real-time detection method based on a dynamic pruning strategy is proposed. First of all, we propose to add a shallow prediction branch to the initial model, which makes the three-scale prediction branch become the four-scale prediction branch. The preliminary improved network expands the detection range and improves the detection accuracy of the model. Secondly, we propose a dynamic channel pruning method to prune the model, which can greatly reduce the amount of calculation without reducing the accuracy of the model, thus improving the speed of traffic sign detection. Thirdly, the weight sharing method is used to replace different weights in the same cluster with the same value, which reduces the occupied hardware resources and realizes rapid deployment of the model. Finally, to increase the number of rare traffic signs, we propose to merge the TT 100 K and CCTSDB into one dataset. The experimental results show that the improved model not only improves the detection accuracy, but also improves the inference time, which is conducive to the deployment of the model on resource-constrained devices. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
| abstract_unstemmed |
Abstract Traffic sign detection can provide important judgment information for the unmanned driving system. To deploy the detector on edge equipment and have better detection performance, a real-time detection method based on a dynamic pruning strategy is proposed. First of all, we propose to add a shallow prediction branch to the initial model, which makes the three-scale prediction branch become the four-scale prediction branch. The preliminary improved network expands the detection range and improves the detection accuracy of the model. Secondly, we propose a dynamic channel pruning method to prune the model, which can greatly reduce the amount of calculation without reducing the accuracy of the model, thus improving the speed of traffic sign detection. Thirdly, the weight sharing method is used to replace different weights in the same cluster with the same value, which reduces the occupied hardware resources and realizes rapid deployment of the model. Finally, to increase the number of rare traffic signs, we propose to merge the TT 100 K and CCTSDB into one dataset. The experimental results show that the improved model not only improves the detection accuracy, but also improves the inference time, which is conducive to the deployment of the model on resource-constrained devices. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. |
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A real-time detection method of multi-scale traffic signs based on dynamic pruning strategy |
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https://dx.doi.org/10.1007/s11042-023-15109-2 |
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Rui, Ting Dai, Juying Shao, Faming Lu, Guanlin Wang, Jinkang |
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Rui, Ting Dai, Juying Shao, Faming Lu, Guanlin Wang, Jinkang |
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10.1007/s11042-023-15109-2 |
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2024-07-03T15:54:06.918Z |
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| score |
7.4015274 |