An end-to-end pollution analysis and detection system using artificial intelligence and object detection algorithms

Environmental pollution is generally a by-product of various human activities. Researchers have studied the dangers and harmful effects of pollutants and environmental pollution for centuries, and many necessary steps have been taken. Modern solutions are being constantly developed to tackle these issues efficiently. Visual pollution analysis and detection is a relatively less studied subject, even though it significantly impacts our daily lives. Building automatic pollution or pollutants detection systems has become increasingly popular due to the modern development of advanced artificial intelligence systems. Although some advances have been made, automated pollution detection is not well-researched or fully understood. This study demonstrates how various object detection models could identify such environmental pollutants and how end-to-end applications can analyze the findings. We trained our dataset on three popular object detection models, YOLOv5, Faster R-CNN (Region-based Convolutional Neural Network), and EfficientDet, and compared their performances. The best Mean Average Precision (mAP) score of 0.85 was achieved by the You Only Look Once (YOLOv5) model using its inbuilt augmentation techniques. Then we built a minimal Android application, using which volunteers or authorities could capture and send images along with their Global Positioning System (GPS) coordinates that might contain visual pollutants. These images and coordinates are stored in the cloud and later used by our local server. The local server utilizes the best-trained visual pollution detection model. It generates heat maps of particular locations, visualizing the condition of visual pollution based on the data stored in the cloud. Along with the heat map, our analysis system provides visual analytics like bar charts and pie charts to summarize a region's condition. In addition, we used Active Learning and Incremental Learning methods to utilize the newly collected dataset by building a semi-autonomous annotation and model upgrading system. This also addresses the data scarcity problem associated with further research on visual pollution. Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Hossain, Md. Yearat [verfasserIn] Nijhum, Ifran Rahman [verfasserIn] Shad, Md. Tazin Morshed [verfasserIn] Sadi, Abu Adnan [verfasserIn] Peyal, Md. Mahmudul Kabir [verfasserIn] Rahman, Rashedur M. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Rechteinformationen:	Open Access Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International ; CC BY-NC-ND 4.0

Schlagwörter:	Machine learning Artificial intelligence Data mining Environmental management Pollution detection system Visual pollution

Übergeordnetes Werk:	Enthalten in: Decision analytics journal - Amsterdam : Elsevier, 2021, 8(2023) vom: Sept., Artikel-ID 100283, Seite 1-14
Übergeordnetes Werk:	volume:8 ; year:2023 ; month:09 ; elocationid:100283 ; pages:1-14

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1016/j.dajour.2023.100283

Katalog-ID:	1886423784

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allfields	10.1016/j.dajour.2023.100283 doi (DE-627)1886423784 (DE-599)KXP1886423784 DE-627 ger DE-627 rda eng Hossain, Md. Yearat verfasserin (DE-588)1326873571 (DE-627)1886427364 aut An end-to-end pollution analysis and detection system using artificial intelligence and object detection algorithms Md. Yearat Hossain, Ifran Rahman Nijhum, Md. Tazin Morshed Shad, Abu Adnan Sadi, Md. Mahmudul Kabir Peyal, Rashedur M. Rahman 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Environmental pollution is generally a by-product of various human activities. Researchers have studied the dangers and harmful effects of pollutants and environmental pollution for centuries, and many necessary steps have been taken. Modern solutions are being constantly developed to tackle these issues efficiently. Visual pollution analysis and detection is a relatively less studied subject, even though it significantly impacts our daily lives. Building automatic pollution or pollutants detection systems has become increasingly popular due to the modern development of advanced artificial intelligence systems. Although some advances have been made, automated pollution detection is not well-researched or fully understood. This study demonstrates how various object detection models could identify such environmental pollutants and how end-to-end applications can analyze the findings. We trained our dataset on three popular object detection models, YOLOv5, Faster R-CNN (Region-based Convolutional Neural Network), and EfficientDet, and compared their performances. The best Mean Average Precision (mAP) score of 0.85 was achieved by the You Only Look Once (YOLOv5) model using its inbuilt augmentation techniques. Then we built a minimal Android application, using which volunteers or authorities could capture and send images along with their Global Positioning System (GPS) coordinates that might contain visual pollutants. These images and coordinates are stored in the cloud and later used by our local server. The local server utilizes the best-trained visual pollution detection model. It generates heat maps of particular locations, visualizing the condition of visual pollution based on the data stored in the cloud. Along with the heat map, our analysis system provides visual analytics like bar charts and pie charts to summarize a region's condition. In addition, we used Active Learning and Incremental Learning methods to utilize the newly collected dataset by building a semi-autonomous annotation and model upgrading system. This also addresses the data scarcity problem associated with further research on visual pollution. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Machine learning (dpeaa)DE-206 Artificial intelligence (dpeaa)DE-206 Data mining (dpeaa)DE-206 Environmental management (dpeaa)DE-206 Pollution detection system (dpeaa)DE-206 Visual pollution (dpeaa)DE-206 Nijhum, Ifran Rahman verfasserin (DE-588)1326874608 (DE-627)1886427747 aut Shad, Md. Tazin Morshed verfasserin (DE-588)1326875337 (DE-627)1886427895 aut Sadi, Abu Adnan verfasserin (DE-588)1326878832 (DE-627)1886428794 aut Peyal, Md. Mahmudul Kabir verfasserin (DE-588)1326880187 (DE-627)1886429081 aut Rahman, Rashedur M. verfasserin aut Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 8(2023) vom: Sept., Artikel-ID 100283, Seite 1-14 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:8 year:2023 month:09 elocationid:100283 pages:1-14 https://www.sciencedirect.com/science/article/pii/S2772662223001236/pdf Verlag kostenfrei https://doi.org/10.1016/j.dajour.2023.100283 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2023 9 100283 1-14 26 01 0206 4514217190 x1z 22-04-24 2403 01 DE-LFER 4521194249 00 --%%-- --%%-- n --%%-- l01 07-05-24 2403 01 DE-LFER https://doi.org/10.1016/j.dajour.2023.100283 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2772662223001236/pdf
spelling	10.1016/j.dajour.2023.100283 doi (DE-627)1886423784 (DE-599)KXP1886423784 DE-627 ger DE-627 rda eng Hossain, Md. Yearat verfasserin (DE-588)1326873571 (DE-627)1886427364 aut An end-to-end pollution analysis and detection system using artificial intelligence and object detection algorithms Md. Yearat Hossain, Ifran Rahman Nijhum, Md. Tazin Morshed Shad, Abu Adnan Sadi, Md. Mahmudul Kabir Peyal, Rashedur M. Rahman 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Environmental pollution is generally a by-product of various human activities. Researchers have studied the dangers and harmful effects of pollutants and environmental pollution for centuries, and many necessary steps have been taken. Modern solutions are being constantly developed to tackle these issues efficiently. Visual pollution analysis and detection is a relatively less studied subject, even though it significantly impacts our daily lives. Building automatic pollution or pollutants detection systems has become increasingly popular due to the modern development of advanced artificial intelligence systems. Although some advances have been made, automated pollution detection is not well-researched or fully understood. This study demonstrates how various object detection models could identify such environmental pollutants and how end-to-end applications can analyze the findings. We trained our dataset on three popular object detection models, YOLOv5, Faster R-CNN (Region-based Convolutional Neural Network), and EfficientDet, and compared their performances. The best Mean Average Precision (mAP) score of 0.85 was achieved by the You Only Look Once (YOLOv5) model using its inbuilt augmentation techniques. Then we built a minimal Android application, using which volunteers or authorities could capture and send images along with their Global Positioning System (GPS) coordinates that might contain visual pollutants. These images and coordinates are stored in the cloud and later used by our local server. The local server utilizes the best-trained visual pollution detection model. It generates heat maps of particular locations, visualizing the condition of visual pollution based on the data stored in the cloud. Along with the heat map, our analysis system provides visual analytics like bar charts and pie charts to summarize a region's condition. In addition, we used Active Learning and Incremental Learning methods to utilize the newly collected dataset by building a semi-autonomous annotation and model upgrading system. This also addresses the data scarcity problem associated with further research on visual pollution. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Machine learning (dpeaa)DE-206 Artificial intelligence (dpeaa)DE-206 Data mining (dpeaa)DE-206 Environmental management (dpeaa)DE-206 Pollution detection system (dpeaa)DE-206 Visual pollution (dpeaa)DE-206 Nijhum, Ifran Rahman verfasserin (DE-588)1326874608 (DE-627)1886427747 aut Shad, Md. Tazin Morshed verfasserin (DE-588)1326875337 (DE-627)1886427895 aut Sadi, Abu Adnan verfasserin (DE-588)1326878832 (DE-627)1886428794 aut Peyal, Md. Mahmudul Kabir verfasserin (DE-588)1326880187 (DE-627)1886429081 aut Rahman, Rashedur M. verfasserin aut Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 8(2023) vom: Sept., Artikel-ID 100283, Seite 1-14 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:8 year:2023 month:09 elocationid:100283 pages:1-14 https://www.sciencedirect.com/science/article/pii/S2772662223001236/pdf Verlag kostenfrei https://doi.org/10.1016/j.dajour.2023.100283 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2023 9 100283 1-14 26 01 0206 4514217190 x1z 22-04-24 2403 01 DE-LFER 4521194249 00 --%%-- --%%-- n --%%-- l01 07-05-24 2403 01 DE-LFER https://doi.org/10.1016/j.dajour.2023.100283 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2772662223001236/pdf
allfields_unstemmed	10.1016/j.dajour.2023.100283 doi (DE-627)1886423784 (DE-599)KXP1886423784 DE-627 ger DE-627 rda eng Hossain, Md. Yearat verfasserin (DE-588)1326873571 (DE-627)1886427364 aut An end-to-end pollution analysis and detection system using artificial intelligence and object detection algorithms Md. Yearat Hossain, Ifran Rahman Nijhum, Md. Tazin Morshed Shad, Abu Adnan Sadi, Md. Mahmudul Kabir Peyal, Rashedur M. Rahman 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Environmental pollution is generally a by-product of various human activities. Researchers have studied the dangers and harmful effects of pollutants and environmental pollution for centuries, and many necessary steps have been taken. Modern solutions are being constantly developed to tackle these issues efficiently. Visual pollution analysis and detection is a relatively less studied subject, even though it significantly impacts our daily lives. Building automatic pollution or pollutants detection systems has become increasingly popular due to the modern development of advanced artificial intelligence systems. Although some advances have been made, automated pollution detection is not well-researched or fully understood. This study demonstrates how various object detection models could identify such environmental pollutants and how end-to-end applications can analyze the findings. We trained our dataset on three popular object detection models, YOLOv5, Faster R-CNN (Region-based Convolutional Neural Network), and EfficientDet, and compared their performances. The best Mean Average Precision (mAP) score of 0.85 was achieved by the You Only Look Once (YOLOv5) model using its inbuilt augmentation techniques. Then we built a minimal Android application, using which volunteers or authorities could capture and send images along with their Global Positioning System (GPS) coordinates that might contain visual pollutants. These images and coordinates are stored in the cloud and later used by our local server. The local server utilizes the best-trained visual pollution detection model. It generates heat maps of particular locations, visualizing the condition of visual pollution based on the data stored in the cloud. Along with the heat map, our analysis system provides visual analytics like bar charts and pie charts to summarize a region's condition. In addition, we used Active Learning and Incremental Learning methods to utilize the newly collected dataset by building a semi-autonomous annotation and model upgrading system. This also addresses the data scarcity problem associated with further research on visual pollution. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Machine learning (dpeaa)DE-206 Artificial intelligence (dpeaa)DE-206 Data mining (dpeaa)DE-206 Environmental management (dpeaa)DE-206 Pollution detection system (dpeaa)DE-206 Visual pollution (dpeaa)DE-206 Nijhum, Ifran Rahman verfasserin (DE-588)1326874608 (DE-627)1886427747 aut Shad, Md. Tazin Morshed verfasserin (DE-588)1326875337 (DE-627)1886427895 aut Sadi, Abu Adnan verfasserin (DE-588)1326878832 (DE-627)1886428794 aut Peyal, Md. Mahmudul Kabir verfasserin (DE-588)1326880187 (DE-627)1886429081 aut Rahman, Rashedur M. verfasserin aut Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 8(2023) vom: Sept., Artikel-ID 100283, Seite 1-14 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:8 year:2023 month:09 elocationid:100283 pages:1-14 https://www.sciencedirect.com/science/article/pii/S2772662223001236/pdf Verlag kostenfrei https://doi.org/10.1016/j.dajour.2023.100283 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2023 9 100283 1-14 26 01 0206 4514217190 x1z 22-04-24 2403 01 DE-LFER 4521194249 00 --%%-- --%%-- n --%%-- l01 07-05-24 2403 01 DE-LFER https://doi.org/10.1016/j.dajour.2023.100283 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2772662223001236/pdf
allfieldsGer	10.1016/j.dajour.2023.100283 doi (DE-627)1886423784 (DE-599)KXP1886423784 DE-627 ger DE-627 rda eng Hossain, Md. Yearat verfasserin (DE-588)1326873571 (DE-627)1886427364 aut An end-to-end pollution analysis and detection system using artificial intelligence and object detection algorithms Md. Yearat Hossain, Ifran Rahman Nijhum, Md. Tazin Morshed Shad, Abu Adnan Sadi, Md. Mahmudul Kabir Peyal, Rashedur M. Rahman 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Environmental pollution is generally a by-product of various human activities. Researchers have studied the dangers and harmful effects of pollutants and environmental pollution for centuries, and many necessary steps have been taken. Modern solutions are being constantly developed to tackle these issues efficiently. Visual pollution analysis and detection is a relatively less studied subject, even though it significantly impacts our daily lives. Building automatic pollution or pollutants detection systems has become increasingly popular due to the modern development of advanced artificial intelligence systems. Although some advances have been made, automated pollution detection is not well-researched or fully understood. This study demonstrates how various object detection models could identify such environmental pollutants and how end-to-end applications can analyze the findings. We trained our dataset on three popular object detection models, YOLOv5, Faster R-CNN (Region-based Convolutional Neural Network), and EfficientDet, and compared their performances. The best Mean Average Precision (mAP) score of 0.85 was achieved by the You Only Look Once (YOLOv5) model using its inbuilt augmentation techniques. Then we built a minimal Android application, using which volunteers or authorities could capture and send images along with their Global Positioning System (GPS) coordinates that might contain visual pollutants. These images and coordinates are stored in the cloud and later used by our local server. The local server utilizes the best-trained visual pollution detection model. It generates heat maps of particular locations, visualizing the condition of visual pollution based on the data stored in the cloud. Along with the heat map, our analysis system provides visual analytics like bar charts and pie charts to summarize a region's condition. In addition, we used Active Learning and Incremental Learning methods to utilize the newly collected dataset by building a semi-autonomous annotation and model upgrading system. This also addresses the data scarcity problem associated with further research on visual pollution. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Machine learning (dpeaa)DE-206 Artificial intelligence (dpeaa)DE-206 Data mining (dpeaa)DE-206 Environmental management (dpeaa)DE-206 Pollution detection system (dpeaa)DE-206 Visual pollution (dpeaa)DE-206 Nijhum, Ifran Rahman verfasserin (DE-588)1326874608 (DE-627)1886427747 aut Shad, Md. Tazin Morshed verfasserin (DE-588)1326875337 (DE-627)1886427895 aut Sadi, Abu Adnan verfasserin (DE-588)1326878832 (DE-627)1886428794 aut Peyal, Md. Mahmudul Kabir verfasserin (DE-588)1326880187 (DE-627)1886429081 aut Rahman, Rashedur M. verfasserin aut Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 8(2023) vom: Sept., Artikel-ID 100283, Seite 1-14 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:8 year:2023 month:09 elocationid:100283 pages:1-14 https://www.sciencedirect.com/science/article/pii/S2772662223001236/pdf Verlag kostenfrei https://doi.org/10.1016/j.dajour.2023.100283 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2023 9 100283 1-14 26 01 0206 4514217190 x1z 22-04-24 2403 01 DE-LFER 4521194249 00 --%%-- --%%-- n --%%-- l01 07-05-24 2403 01 DE-LFER https://doi.org/10.1016/j.dajour.2023.100283 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2772662223001236/pdf
allfieldsSound	10.1016/j.dajour.2023.100283 doi (DE-627)1886423784 (DE-599)KXP1886423784 DE-627 ger DE-627 rda eng Hossain, Md. Yearat verfasserin (DE-588)1326873571 (DE-627)1886427364 aut An end-to-end pollution analysis and detection system using artificial intelligence and object detection algorithms Md. Yearat Hossain, Ifran Rahman Nijhum, Md. Tazin Morshed Shad, Abu Adnan Sadi, Md. Mahmudul Kabir Peyal, Rashedur M. Rahman 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier DE-206 Open Access Controlled Vocabulary for Access Rights http://purl.org/coar/access_right/c_abf2 Environmental pollution is generally a by-product of various human activities. Researchers have studied the dangers and harmful effects of pollutants and environmental pollution for centuries, and many necessary steps have been taken. Modern solutions are being constantly developed to tackle these issues efficiently. Visual pollution analysis and detection is a relatively less studied subject, even though it significantly impacts our daily lives. Building automatic pollution or pollutants detection systems has become increasingly popular due to the modern development of advanced artificial intelligence systems. Although some advances have been made, automated pollution detection is not well-researched or fully understood. This study demonstrates how various object detection models could identify such environmental pollutants and how end-to-end applications can analyze the findings. We trained our dataset on three popular object detection models, YOLOv5, Faster R-CNN (Region-based Convolutional Neural Network), and EfficientDet, and compared their performances. The best Mean Average Precision (mAP) score of 0.85 was achieved by the You Only Look Once (YOLOv5) model using its inbuilt augmentation techniques. Then we built a minimal Android application, using which volunteers or authorities could capture and send images along with their Global Positioning System (GPS) coordinates that might contain visual pollutants. These images and coordinates are stored in the cloud and later used by our local server. The local server utilizes the best-trained visual pollution detection model. It generates heat maps of particular locations, visualizing the condition of visual pollution based on the data stored in the cloud. Along with the heat map, our analysis system provides visual analytics like bar charts and pie charts to summarize a region's condition. In addition, we used Active Learning and Incremental Learning methods to utilize the newly collected dataset by building a semi-autonomous annotation and model upgrading system. This also addresses the data scarcity problem associated with further research on visual pollution. DE-206 Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International CC BY-NC-ND 4.0 cc https://creativecommons.org/licenses/by-nc-nd/4.0/ Machine learning (dpeaa)DE-206 Artificial intelligence (dpeaa)DE-206 Data mining (dpeaa)DE-206 Environmental management (dpeaa)DE-206 Pollution detection system (dpeaa)DE-206 Visual pollution (dpeaa)DE-206 Nijhum, Ifran Rahman verfasserin (DE-588)1326874608 (DE-627)1886427747 aut Shad, Md. Tazin Morshed verfasserin (DE-588)1326875337 (DE-627)1886427895 aut Sadi, Abu Adnan verfasserin (DE-588)1326878832 (DE-627)1886428794 aut Peyal, Md. Mahmudul Kabir verfasserin (DE-588)1326880187 (DE-627)1886429081 aut Rahman, Rashedur M. verfasserin aut Enthalten in Decision analytics journal Amsterdam : Elsevier, 2021 8(2023) vom: Sept., Artikel-ID 100283, Seite 1-14 Online-Ressource (DE-627)178621072X (DE-600)3106160-6 2772-6622 nnns volume:8 year:2023 month:09 elocationid:100283 pages:1-14 https://www.sciencedirect.com/science/article/pii/S2772662223001236/pdf Verlag kostenfrei https://doi.org/10.1016/j.dajour.2023.100283 Resolving-System kostenfrei GBV_USEFLAG_U GBV_ILN_26 ISIL_DE-206 SYSFLAG_1 GBV_KXP GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 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_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 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_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 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_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 GBV_ILN_2403 GBV_ILN_2403 ISIL_DE-LFER AR 8 2023 9 100283 1-14 26 01 0206 4514217190 x1z 22-04-24 2403 01 DE-LFER 4521194249 00 --%%-- --%%-- n --%%-- l01 07-05-24 2403 01 DE-LFER https://doi.org/10.1016/j.dajour.2023.100283 2403 01 DE-LFER https://www.sciencedirect.com/science/article/pii/S2772662223001236/pdf
language	English
source	Enthalten in Decision analytics journal 8(2023) vom: Sept., Artikel-ID 100283, Seite 1-14 volume:8 year:2023 month:09 elocationid:100283 pages:1-14
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topic_facet	Machine learning Artificial intelligence Data mining Environmental management Pollution detection system Visual pollution
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authorswithroles_txt_mv	Hossain, Md. Yearat @@aut@@ Nijhum, Ifran Rahman @@aut@@ Shad, Md. Tazin Morshed @@aut@@ Sadi, Abu Adnan @@aut@@ Peyal, Md. Mahmudul Kabir @@aut@@ Rahman, Rashedur M. @@aut@@
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Researchers have studied the dangers and harmful effects of pollutants and environmental pollution for centuries, and many necessary steps have been taken. Modern solutions are being constantly developed to tackle these issues efficiently. Visual pollution analysis and detection is a relatively less studied subject, even though it significantly impacts our daily lives. Building automatic pollution or pollutants detection systems has become increasingly popular due to the modern development of advanced artificial intelligence systems. Although some advances have been made, automated pollution detection is not well-researched or fully understood. This study demonstrates how various object detection models could identify such environmental pollutants and how end-to-end applications can analyze the findings. We trained our dataset on three popular object detection models, YOLOv5, Faster R-CNN (Region-based Convolutional Neural Network), and EfficientDet, and compared their performances. The best Mean Average Precision (mAP) score of 0.85 was achieved by the You Only Look Once (YOLOv5) model using its inbuilt augmentation techniques. Then we built a minimal Android application, using which volunteers or authorities could capture and send images along with their Global Positioning System (GPS) coordinates that might contain visual pollutants. These images and coordinates are stored in the cloud and later used by our local server. The local server utilizes the best-trained visual pollution detection model. It generates heat maps of particular locations, visualizing the condition of visual pollution based on the data stored in the cloud. Along with the heat map, our analysis system provides visual analytics like bar charts and pie charts to summarize a region's condition. In addition, we used Active Learning and Incremental Learning methods to utilize the newly collected dataset by building a semi-autonomous annotation and model upgrading system. 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topic_title	An end-to-end pollution analysis and detection system using artificial intelligence and object detection algorithms Md. Yearat Hossain, Ifran Rahman Nijhum, Md. Tazin Morshed Shad, Abu Adnan Sadi, Md. Mahmudul Kabir Peyal, Rashedur M. Rahman Machine learning (dpeaa)DE-206 Artificial intelligence (dpeaa)DE-206 Data mining (dpeaa)DE-206 Environmental management (dpeaa)DE-206 Pollution detection system (dpeaa)DE-206 Visual pollution (dpeaa)DE-206
topic	misc Machine learning misc Artificial intelligence misc Data mining misc Environmental management misc Pollution detection system misc Visual pollution
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title_full	An end-to-end pollution analysis and detection system using artificial intelligence and object detection algorithms Md. Yearat Hossain, Ifran Rahman Nijhum, Md. Tazin Morshed Shad, Abu Adnan Sadi, Md. Mahmudul Kabir Peyal, Rashedur M. Rahman
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title_sort	end-to-end pollution analysis and detection system using artificial intelligence and object detection algorithms
title_auth	An end-to-end pollution analysis and detection system using artificial intelligence and object detection algorithms
abstract	Environmental pollution is generally a by-product of various human activities. Researchers have studied the dangers and harmful effects of pollutants and environmental pollution for centuries, and many necessary steps have been taken. Modern solutions are being constantly developed to tackle these issues efficiently. Visual pollution analysis and detection is a relatively less studied subject, even though it significantly impacts our daily lives. Building automatic pollution or pollutants detection systems has become increasingly popular due to the modern development of advanced artificial intelligence systems. Although some advances have been made, automated pollution detection is not well-researched or fully understood. This study demonstrates how various object detection models could identify such environmental pollutants and how end-to-end applications can analyze the findings. We trained our dataset on three popular object detection models, YOLOv5, Faster R-CNN (Region-based Convolutional Neural Network), and EfficientDet, and compared their performances. The best Mean Average Precision (mAP) score of 0.85 was achieved by the You Only Look Once (YOLOv5) model using its inbuilt augmentation techniques. Then we built a minimal Android application, using which volunteers or authorities could capture and send images along with their Global Positioning System (GPS) coordinates that might contain visual pollutants. These images and coordinates are stored in the cloud and later used by our local server. The local server utilizes the best-trained visual pollution detection model. It generates heat maps of particular locations, visualizing the condition of visual pollution based on the data stored in the cloud. Along with the heat map, our analysis system provides visual analytics like bar charts and pie charts to summarize a region's condition. In addition, we used Active Learning and Incremental Learning methods to utilize the newly collected dataset by building a semi-autonomous annotation and model upgrading system. This also addresses the data scarcity problem associated with further research on visual pollution.
abstractGer	Environmental pollution is generally a by-product of various human activities. Researchers have studied the dangers and harmful effects of pollutants and environmental pollution for centuries, and many necessary steps have been taken. Modern solutions are being constantly developed to tackle these issues efficiently. Visual pollution analysis and detection is a relatively less studied subject, even though it significantly impacts our daily lives. Building automatic pollution or pollutants detection systems has become increasingly popular due to the modern development of advanced artificial intelligence systems. Although some advances have been made, automated pollution detection is not well-researched or fully understood. This study demonstrates how various object detection models could identify such environmental pollutants and how end-to-end applications can analyze the findings. We trained our dataset on three popular object detection models, YOLOv5, Faster R-CNN (Region-based Convolutional Neural Network), and EfficientDet, and compared their performances. The best Mean Average Precision (mAP) score of 0.85 was achieved by the You Only Look Once (YOLOv5) model using its inbuilt augmentation techniques. Then we built a minimal Android application, using which volunteers or authorities could capture and send images along with their Global Positioning System (GPS) coordinates that might contain visual pollutants. These images and coordinates are stored in the cloud and later used by our local server. The local server utilizes the best-trained visual pollution detection model. It generates heat maps of particular locations, visualizing the condition of visual pollution based on the data stored in the cloud. Along with the heat map, our analysis system provides visual analytics like bar charts and pie charts to summarize a region's condition. In addition, we used Active Learning and Incremental Learning methods to utilize the newly collected dataset by building a semi-autonomous annotation and model upgrading system. This also addresses the data scarcity problem associated with further research on visual pollution.
abstract_unstemmed	Environmental pollution is generally a by-product of various human activities. Researchers have studied the dangers and harmful effects of pollutants and environmental pollution for centuries, and many necessary steps have been taken. Modern solutions are being constantly developed to tackle these issues efficiently. Visual pollution analysis and detection is a relatively less studied subject, even though it significantly impacts our daily lives. Building automatic pollution or pollutants detection systems has become increasingly popular due to the modern development of advanced artificial intelligence systems. Although some advances have been made, automated pollution detection is not well-researched or fully understood. This study demonstrates how various object detection models could identify such environmental pollutants and how end-to-end applications can analyze the findings. We trained our dataset on three popular object detection models, YOLOv5, Faster R-CNN (Region-based Convolutional Neural Network), and EfficientDet, and compared their performances. The best Mean Average Precision (mAP) score of 0.85 was achieved by the You Only Look Once (YOLOv5) model using its inbuilt augmentation techniques. Then we built a minimal Android application, using which volunteers or authorities could capture and send images along with their Global Positioning System (GPS) coordinates that might contain visual pollutants. These images and coordinates are stored in the cloud and later used by our local server. The local server utilizes the best-trained visual pollution detection model. It generates heat maps of particular locations, visualizing the condition of visual pollution based on the data stored in the cloud. Along with the heat map, our analysis system provides visual analytics like bar charts and pie charts to summarize a region's condition. In addition, we used Active Learning and Incremental Learning methods to utilize the newly collected dataset by building a semi-autonomous annotation and model upgrading system. This also addresses the data scarcity problem associated with further research on visual pollution.
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title_short	An end-to-end pollution analysis and detection system using artificial intelligence and object detection algorithms
url	https://www.sciencedirect.com/science/article/pii/S2772662223001236/pdf https://doi.org/10.1016/j.dajour.2023.100283
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author2	Nijhum, Ifran Rahman Shad, Md. Tazin Morshed Sadi, Abu Adnan Peyal, Md. Mahmudul Kabir Rahman, Rashedur M.
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up_date	2024-07-04T21:18:03.334Z
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An end-to-end pollution analysis and detection system using artificial intelligence and object detection algorithms

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