| dc.contributor.author | Bilak, Yuliana | |
| dc.contributor.author | Tonenchuk, Tetiana | |
| dc.date.accessioned | 2024-12-10T11:49:31Z | |
| dc.date.available | 2024-12-10T11:49:31Z | |
| dc.date.issued | 2024 | |
| dc.identifier.citation | Bilak Yu., Tonenchuk T. Application of Computer Vision for Safer and More Efficient Urban Environments. Science and Technology of the XXI Century: Proceedings of the International R&D Online Student Conference and Competition, 04 December, 2024. Kyiv, 2024. Part ІІ. P. 89-91. | uk_UA |
| dc.identifier.issn | 2411-3050 | |
| dc.identifier.uri | https://archer.chnu.edu.ua/xmlui/handle/123456789/11063 | |
| dc.description.abstract | The rapid growth of urban populations and the increasing
complexity of city infrastructure demand innovative solutions to enhance urban living
and ensure public safety. Computer vision technologies based on convolutional
neural networks (CNNs) are becoming one of the key technologies for building smart
cities, providing real-time automatic analysis of urban environments.The purpose of this work is to analyze the application of computer
vision technologies based on convolutional neural networks (CNNs) to enhance
public safety and optimize urban infrastructure in the context of smart cities. | uk_UA |
| dc.language.iso | en | uk_UA |
| dc.publisher | National Technical University of Ukraine ‘Igor Sikorsky Kyiv Polytechnic Institute’ | uk_UA |
| dc.subject | computer vision | uk_UA |
| dc.subject | convolutional neural networks | uk_UA |
| dc.subject | urban infrastructure | uk_UA |
| dc.subject | object recognition | uk_UA |
| dc.subject | public safety | uk_UA |
| dc.subject | traffic management | uk_UA |
| dc.title | Application of Computer Vision for Safer and More Efficient Urban Environments | uk_UA |
| dc.type | Article | uk_UA |
