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DC Field | Value | Language |
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dc.contributor.author | Dr. NAVEEN KUMAR G. N. | - |
dc.date.accessioned | 2022-10-14T15:53:59Z | - |
dc.date.available | 2022-10-14T15:53:59Z | - |
dc.date.issued | 2022 | - |
dc.identifier.issn | 0493-2137 | - |
dc.identifier.uri | http://localhost:8080/xmlui/handle/123456789/6665 | - |
dc.description.abstract | The rapid growth of the civilization has made our lives easier and increases the auto vehicles usage to a great extent. With great increase in the usage of cars and automotive vehicles, chances of getting accidents are also very high. India needs to improve the way they respond to the road accidents this is a system that can help in the identifying the severity of the accident and detect the accident using deep learning and computer vision techniques. The project aims to monitor the accident in cities and to reduce the death rates. Nowadays, road accident rates are very high. Early detection and timely medical aid will help a lot in these situations. Regular traffic systems are implemented with cameras and installed in most of the town to watch and control traffic. A Smart City with an AI traffic monitoring and reporting mechanism, a more superior traffic monitoring method may recognize and discover moving objects like automobiles and motorbikes in live camera supports. Furthermore, detect collision of those moving objects and helps to provide an accurate location to the nearby center about the accident to supply immediate medical care and sends a message to the closest police headquarters. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | Tianjin Daxue Xuebao (Ziran Kexue yu Gongcheng Jishu Ban)/ Journal of Tianjin University Science and Technology | en_US |
dc.subject | Electronics Communication / Telecommunication | en_US |
dc.title | VEHICULAR DISASTER IMPACT IDENTIFICATION USING DEEP LEARNING | en_US |
dc.title.alternative | Electronics Communication / Telecommunication | en_US |
dc.type | Article | en_US |
Appears in Collections: | 2022 |
Files in This Item:
File | Description | Size | Format | |
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7. PAPER7ECE.pdf | 181.56 kB | Adobe PDF | View/Open |
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