Development and optimization of image fire detection on deep learning algorithms

Abstract The primary function of fire detection is to detect fires and raise the alarm early. A detection algorithm is a key element of image fire detection (IFD) technology because it directly determines the IFD’s performance. In this study, an IFD algorithm based on the YOLOv3 network was develope...
Ausführliche Beschreibung

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Autor*in:	Yang, Yi [verfasserIn] Pan, Mengyi Li, Pu Wang, Xuefeng Tsai, Yun-Ting

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2022

Schlagwörter:	Image fire detection YOLOv3 network Detection ability Average accuracy Detection speed

Anmerkung:	© Akadémiai Kiadó, Budapest, Hungary 2022. Springer Nature or its licensor 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.

Übergeordnetes Werk:	Enthalten in: Journal of thermal analysis and calorimetry - Dordrecht [u.a.] : Springer Science + Business Media B.V., 1969, 148(2022), 11 vom: 16. Okt., Seite 5089-5095
Übergeordnetes Werk:	volume:148 ; year:2022 ; number:11 ; day:16 ; month:10 ; pages:5089-5095

Links:	Volltext

DOI / URN:	10.1007/s10973-022-11657-1

Katalog-ID:	SPR051580942

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520			\|a Abstract The primary function of fire detection is to detect fires and raise the alarm early. A detection algorithm is a key element of image fire detection (IFD) technology because it directly determines the IFD’s performance. In this study, an IFD algorithm based on the YOLOv3 network was developed to detect smoke and flame simultaneously. Subsequently, six improvements were applied to promote the algorithm’s ability to detect fire early. The results demonstrated that the modified YOLOv3 network achieved an average accuracy of 95%, which is 14.1% higher than that of the same model without modifications. The detection speed reached 22 Frames Per Second (FPS), which satisfies the requirements of real-time detection.
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allfields	10.1007/s10973-022-11657-1 doi (DE-627)SPR051580942 (SPR)s10973-022-11657-1-e DE-627 ger DE-627 rakwb eng Yang, Yi verfasserin (orcid)0000-0001-7797-6341 aut Development and optimization of image fire detection on deep learning algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022. Springer Nature or its licensor 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 The primary function of fire detection is to detect fires and raise the alarm early. A detection algorithm is a key element of image fire detection (IFD) technology because it directly determines the IFD’s performance. In this study, an IFD algorithm based on the YOLOv3 network was developed to detect smoke and flame simultaneously. Subsequently, six improvements were applied to promote the algorithm’s ability to detect fire early. The results demonstrated that the modified YOLOv3 network achieved an average accuracy of 95%, which is 14.1% higher than that of the same model without modifications. The detection speed reached 22 Frames Per Second (FPS), which satisfies the requirements of real-time detection. Image fire detection (dpeaa)DE-He213 YOLOv3 network (dpeaa)DE-He213 Detection ability (dpeaa)DE-He213 Average accuracy (dpeaa)DE-He213 Detection speed (dpeaa)DE-He213 Pan, Mengyi aut Li, Pu aut Wang, Xuefeng aut Tsai, Yun-Ting aut Enthalten in Journal of thermal analysis and calorimetry Dordrecht [u.a.] : Springer Science + Business Media B.V., 1969 148(2022), 11 vom: 16. Okt., Seite 5089-5095 (DE-627)315295422 (DE-600)2017304-0 1572-8943 nnns volume:148 year:2022 number:11 day:16 month:10 pages:5089-5095 https://dx.doi.org/10.1007/s10973-022-11657-1 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_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_206 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 148 2022 11 16 10 5089-5095
spelling	10.1007/s10973-022-11657-1 doi (DE-627)SPR051580942 (SPR)s10973-022-11657-1-e DE-627 ger DE-627 rakwb eng Yang, Yi verfasserin (orcid)0000-0001-7797-6341 aut Development and optimization of image fire detection on deep learning algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022. Springer Nature or its licensor 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 The primary function of fire detection is to detect fires and raise the alarm early. A detection algorithm is a key element of image fire detection (IFD) technology because it directly determines the IFD’s performance. In this study, an IFD algorithm based on the YOLOv3 network was developed to detect smoke and flame simultaneously. Subsequently, six improvements were applied to promote the algorithm’s ability to detect fire early. The results demonstrated that the modified YOLOv3 network achieved an average accuracy of 95%, which is 14.1% higher than that of the same model without modifications. The detection speed reached 22 Frames Per Second (FPS), which satisfies the requirements of real-time detection. Image fire detection (dpeaa)DE-He213 YOLOv3 network (dpeaa)DE-He213 Detection ability (dpeaa)DE-He213 Average accuracy (dpeaa)DE-He213 Detection speed (dpeaa)DE-He213 Pan, Mengyi aut Li, Pu aut Wang, Xuefeng aut Tsai, Yun-Ting aut Enthalten in Journal of thermal analysis and calorimetry Dordrecht [u.a.] : Springer Science + Business Media B.V., 1969 148(2022), 11 vom: 16. Okt., Seite 5089-5095 (DE-627)315295422 (DE-600)2017304-0 1572-8943 nnns volume:148 year:2022 number:11 day:16 month:10 pages:5089-5095 https://dx.doi.org/10.1007/s10973-022-11657-1 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_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_206 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 148 2022 11 16 10 5089-5095
allfields_unstemmed	10.1007/s10973-022-11657-1 doi (DE-627)SPR051580942 (SPR)s10973-022-11657-1-e DE-627 ger DE-627 rakwb eng Yang, Yi verfasserin (orcid)0000-0001-7797-6341 aut Development and optimization of image fire detection on deep learning algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022. Springer Nature or its licensor 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 The primary function of fire detection is to detect fires and raise the alarm early. A detection algorithm is a key element of image fire detection (IFD) technology because it directly determines the IFD’s performance. In this study, an IFD algorithm based on the YOLOv3 network was developed to detect smoke and flame simultaneously. Subsequently, six improvements were applied to promote the algorithm’s ability to detect fire early. The results demonstrated that the modified YOLOv3 network achieved an average accuracy of 95%, which is 14.1% higher than that of the same model without modifications. The detection speed reached 22 Frames Per Second (FPS), which satisfies the requirements of real-time detection. Image fire detection (dpeaa)DE-He213 YOLOv3 network (dpeaa)DE-He213 Detection ability (dpeaa)DE-He213 Average accuracy (dpeaa)DE-He213 Detection speed (dpeaa)DE-He213 Pan, Mengyi aut Li, Pu aut Wang, Xuefeng aut Tsai, Yun-Ting aut Enthalten in Journal of thermal analysis and calorimetry Dordrecht [u.a.] : Springer Science + Business Media B.V., 1969 148(2022), 11 vom: 16. Okt., Seite 5089-5095 (DE-627)315295422 (DE-600)2017304-0 1572-8943 nnns volume:148 year:2022 number:11 day:16 month:10 pages:5089-5095 https://dx.doi.org/10.1007/s10973-022-11657-1 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_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_206 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 148 2022 11 16 10 5089-5095
allfieldsGer	10.1007/s10973-022-11657-1 doi (DE-627)SPR051580942 (SPR)s10973-022-11657-1-e DE-627 ger DE-627 rakwb eng Yang, Yi verfasserin (orcid)0000-0001-7797-6341 aut Development and optimization of image fire detection on deep learning algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022. Springer Nature or its licensor 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 The primary function of fire detection is to detect fires and raise the alarm early. A detection algorithm is a key element of image fire detection (IFD) technology because it directly determines the IFD’s performance. In this study, an IFD algorithm based on the YOLOv3 network was developed to detect smoke and flame simultaneously. Subsequently, six improvements were applied to promote the algorithm’s ability to detect fire early. The results demonstrated that the modified YOLOv3 network achieved an average accuracy of 95%, which is 14.1% higher than that of the same model without modifications. The detection speed reached 22 Frames Per Second (FPS), which satisfies the requirements of real-time detection. Image fire detection (dpeaa)DE-He213 YOLOv3 network (dpeaa)DE-He213 Detection ability (dpeaa)DE-He213 Average accuracy (dpeaa)DE-He213 Detection speed (dpeaa)DE-He213 Pan, Mengyi aut Li, Pu aut Wang, Xuefeng aut Tsai, Yun-Ting aut Enthalten in Journal of thermal analysis and calorimetry Dordrecht [u.a.] : Springer Science + Business Media B.V., 1969 148(2022), 11 vom: 16. Okt., Seite 5089-5095 (DE-627)315295422 (DE-600)2017304-0 1572-8943 nnns volume:148 year:2022 number:11 day:16 month:10 pages:5089-5095 https://dx.doi.org/10.1007/s10973-022-11657-1 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_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_206 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 148 2022 11 16 10 5089-5095
allfieldsSound	10.1007/s10973-022-11657-1 doi (DE-627)SPR051580942 (SPR)s10973-022-11657-1-e DE-627 ger DE-627 rakwb eng Yang, Yi verfasserin (orcid)0000-0001-7797-6341 aut Development and optimization of image fire detection on deep learning algorithms 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Akadémiai Kiadó, Budapest, Hungary 2022. Springer Nature or its licensor 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 The primary function of fire detection is to detect fires and raise the alarm early. A detection algorithm is a key element of image fire detection (IFD) technology because it directly determines the IFD’s performance. In this study, an IFD algorithm based on the YOLOv3 network was developed to detect smoke and flame simultaneously. Subsequently, six improvements were applied to promote the algorithm’s ability to detect fire early. The results demonstrated that the modified YOLOv3 network achieved an average accuracy of 95%, which is 14.1% higher than that of the same model without modifications. The detection speed reached 22 Frames Per Second (FPS), which satisfies the requirements of real-time detection. Image fire detection (dpeaa)DE-He213 YOLOv3 network (dpeaa)DE-He213 Detection ability (dpeaa)DE-He213 Average accuracy (dpeaa)DE-He213 Detection speed (dpeaa)DE-He213 Pan, Mengyi aut Li, Pu aut Wang, Xuefeng aut Tsai, Yun-Ting aut Enthalten in Journal of thermal analysis and calorimetry Dordrecht [u.a.] : Springer Science + Business Media B.V., 1969 148(2022), 11 vom: 16. Okt., Seite 5089-5095 (DE-627)315295422 (DE-600)2017304-0 1572-8943 nnns volume:148 year:2022 number:11 day:16 month:10 pages:5089-5095 https://dx.doi.org/10.1007/s10973-022-11657-1 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_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_206 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 148 2022 11 16 10 5089-5095
language	English
source	Enthalten in Journal of thermal analysis and calorimetry 148(2022), 11 vom: 16. Okt., Seite 5089-5095 volume:148 year:2022 number:11 day:16 month:10 pages:5089-5095
sourceStr	Enthalten in Journal of thermal analysis and calorimetry 148(2022), 11 vom: 16. Okt., Seite 5089-5095 volume:148 year:2022 number:11 day:16 month:10 pages:5089-5095
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Image fire detection YOLOv3 network Detection ability Average accuracy Detection speed
isfreeaccess_bool	false
container_title	Journal of thermal analysis and calorimetry
authorswithroles_txt_mv	Yang, Yi @@aut@@ Pan, Mengyi @@aut@@ Li, Pu @@aut@@ Wang, Xuefeng @@aut@@ Tsai, Yun-Ting @@aut@@
publishDateDaySort_date	2022-10-16T00:00:00Z
hierarchy_top_id	315295422
id	SPR051580942
language_de	englisch
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author	Yang, Yi
spellingShingle	Yang, Yi misc Image fire detection misc YOLOv3 network misc Detection ability misc Average accuracy misc Detection speed Development and optimization of image fire detection on deep learning algorithms
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topic_title	Development and optimization of image fire detection on deep learning algorithms Image fire detection (dpeaa)DE-He213 YOLOv3 network (dpeaa)DE-He213 Detection ability (dpeaa)DE-He213 Average accuracy (dpeaa)DE-He213 Detection speed (dpeaa)DE-He213
topic	misc Image fire detection misc YOLOv3 network misc Detection ability misc Average accuracy misc Detection speed
topic_unstemmed	misc Image fire detection misc YOLOv3 network misc Detection ability misc Average accuracy misc Detection speed
topic_browse	misc Image fire detection misc YOLOv3 network misc Detection ability misc Average accuracy misc Detection speed
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title	Development and optimization of image fire detection on deep learning algorithms
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title_full	Development and optimization of image fire detection on deep learning algorithms
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author_browse	Yang, Yi Pan, Mengyi Li, Pu Wang, Xuefeng Tsai, Yun-Ting
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title_sort	development and optimization of image fire detection on deep learning algorithms
title_auth	Development and optimization of image fire detection on deep learning algorithms
abstract	Abstract The primary function of fire detection is to detect fires and raise the alarm early. A detection algorithm is a key element of image fire detection (IFD) technology because it directly determines the IFD’s performance. In this study, an IFD algorithm based on the YOLOv3 network was developed to detect smoke and flame simultaneously. Subsequently, six improvements were applied to promote the algorithm’s ability to detect fire early. The results demonstrated that the modified YOLOv3 network achieved an average accuracy of 95%, which is 14.1% higher than that of the same model without modifications. The detection speed reached 22 Frames Per Second (FPS), which satisfies the requirements of real-time detection. © Akadémiai Kiadó, Budapest, Hungary 2022. Springer Nature or its licensor 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 The primary function of fire detection is to detect fires and raise the alarm early. A detection algorithm is a key element of image fire detection (IFD) technology because it directly determines the IFD’s performance. In this study, an IFD algorithm based on the YOLOv3 network was developed to detect smoke and flame simultaneously. Subsequently, six improvements were applied to promote the algorithm’s ability to detect fire early. The results demonstrated that the modified YOLOv3 network achieved an average accuracy of 95%, which is 14.1% higher than that of the same model without modifications. The detection speed reached 22 Frames Per Second (FPS), which satisfies the requirements of real-time detection. © Akadémiai Kiadó, Budapest, Hungary 2022. Springer Nature or its licensor 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 The primary function of fire detection is to detect fires and raise the alarm early. A detection algorithm is a key element of image fire detection (IFD) technology because it directly determines the IFD’s performance. In this study, an IFD algorithm based on the YOLOv3 network was developed to detect smoke and flame simultaneously. Subsequently, six improvements were applied to promote the algorithm’s ability to detect fire early. The results demonstrated that the modified YOLOv3 network achieved an average accuracy of 95%, which is 14.1% higher than that of the same model without modifications. The detection speed reached 22 Frames Per Second (FPS), which satisfies the requirements of real-time detection. © Akadémiai Kiadó, Budapest, Hungary 2022. Springer Nature or its licensor 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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container_issue	11
title_short	Development and optimization of image fire detection on deep learning algorithms
url	https://dx.doi.org/10.1007/s10973-022-11657-1
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author2	Pan, Mengyi Li, Pu Wang, Xuefeng Tsai, Yun-Ting
author2Str	Pan, Mengyi Li, Pu Wang, Xuefeng Tsai, Yun-Ting
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doi_str	10.1007/s10973-022-11657-1
up_date	2024-07-03T22:38:59.770Z
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