Application of Deep Learning: Vision-Based Discernment and Counting of Vehicles inLow Illumination Using Deep Learning - Brossura
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The intelligent transportation system has received a lot of attention in recent years. Vehicle detection is an important task in this area where vehicle counting and classification are two important applications. Taking the size of vehicles and illumination factor into consideration the detection remains a challenge. To overcome the above problem we propose aVision-Based Discernment and Counting of Vehicles in Low Illumination Using Deep Learning, In the proposed system, we enhance the low illumination image using the Dual-channel prior-based method. By using the segmentation method the highway road is first extracted and biased into two areas namely remote area and proximal area. Here we use the YOLOv3 network, by placing the above two areas in this network, we can detect the type and position of the vehicle. Finally, by using the ORB algorithm we can judge the direction of the vehicle and obtain a number of different vehicles. The results obtained are such that the accurate count of vehicles especially for small-sized vehicles in low illumination.
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Application of Deep Learning
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LAP LAMBERT Academic Publishing Feb 2022, 2022
ISBN 10: 6204742426
ISBN 13: 9786204742427
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -The intelligent transportation system has received a lot of attention in recent years. Vehicle detection is an important task in this area where vehicle counting and classification are two important applications. Taking the size of vehicles and illumination factor into consideration the detection remains a challenge. To overcome the above problem we propose aVision-Based Discernment and Counting of Vehicles in Low Illumination Using Deep Learning, In the proposed system, we enhance the low illumination image using the Dual-channel prior-based method. By using the segmentation method the highway road is first extracted and biased into two areas namely remote area and proximal area. Here we use the YOLOv3 network, by placing the above two areas in this network, we can detect the type and position of the vehicle. Finally, by using the ORB algorithm we can judge the direction of the vehicle and obtain a number of different vehicles. The results obtained are such that the accurate count of vehicles especially for small-sized vehicles in low illumination. 108 pp. Englisch. Codice articolo 9786204742427
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Application of Deep Learning
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LAP Lambert Academic Publishing, 2022
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ISBN 13: 9786204742427
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Da: moluna, Greven, Germania
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Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. The intelligent transportation system has received a lot of attention in recent years. Vehicle detection is an important task in this area where vehicle counting and classification are two important applications. Taking the size of vehicles and illumination. Codice articolo 573741097
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Application of Deep Learning
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LAP LAMBERT Academic Publishing Feb 2022, 2022
ISBN 10: 6204742426
ISBN 13: 9786204742427
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germania
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -The intelligent transportation system has received a lot of attention in recent years. Vehicle detection is an important task in this area where vehicle counting and classification are two important applications. Taking the size of vehicles and illumination factor into consideration the detection remains a challenge. To overcome the above problem we propose aVision-Based Discernment and Counting of Vehicles in Low Illumination Using Deep Learning, In the proposed system, we enhance the low illumination image using the Dual-channel prior-based method. By using the segmentation method the highway road is first extracted and biased into two areas namely remote area and proximal area. Here we use the YOLOv3 network, by placing the above two areas in this network, we can detect the type and position of the vehicle. Finally, by using the ORB algorithm we can judge the direction of the vehicle and obtain a number of different vehicles. The results obtained are such that the accurate count of vehicles especially for small-sized vehicles in low illumination.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 108 pp. Englisch. Codice articolo 9786204742427
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Application of Deep Learning | Vision-Based Discernment and Counting of Vehicles inLow Illumination Using Deep Learning
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LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6204742426
ISBN 13: 9786204742427
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Da: preigu, Osnabrück, Germania
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Taschenbuch. Condizione: Neu. Application of Deep Learning | Vision-Based Discernment and Counting of Vehicles inLow Illumination Using Deep Learning | Shafiulla Basha Shaik (u. a.) | Taschenbuch | Englisch | 2022 | LAP LAMBERT Academic Publishing | EAN 9786204742427 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu. Codice articolo 121351693
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Application of Deep Learning : Vision-Based Discernment and Counting of Vehicles inLow Illumination Using Deep Learning
Editore:
LAP LAMBERT Academic Publishing, 2022
ISBN 10: 6204742426
ISBN 13: 9786204742427
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Da: AHA-BUCH GmbH, Einbeck, Germania
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Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - The intelligent transportation system has received a lot of attention in recent years. Vehicle detection is an important task in this area where vehicle counting and classification are two important applications. Taking the size of vehicles and illumination factor into consideration the detection remains a challenge. To overcome the above problem we propose aVision-Based Discernment and Counting of Vehicles in Low Illumination Using Deep Learning, In the proposed system, we enhance the low illumination image using the Dual-channel prior-based method. By using the segmentation method the highway road is first extracted and biased into two areas namely remote area and proximal area. Here we use the YOLOv3 network, by placing the above two areas in this network, we can detect the type and position of the vehicle. Finally, by using the ORB algorithm we can judge the direction of the vehicle and obtain a number of different vehicles. The results obtained are such that the accurate count of vehicles especially for small-sized vehicles in low illumination. Codice articolo 9786204742427
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