![](/image/LogoVKU.png)
Please use this identifier to cite or link to this item:
https://elib.vku.udn.vn/handle/123456789/2698
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Doan, Trung Son | - |
dc.contributor.author | Nguyen, Thi Khanh Tram | - |
dc.contributor.author | Vo, Thai Anh | - |
dc.date.accessioned | 2023-09-25T07:57:26Z | - |
dc.date.available | 2023-09-25T07:57:26Z | - |
dc.date.issued | 2023-06 | - |
dc.identifier.isbn | 978-604-80-8083-9 | - |
dc.identifier.uri | http://elib.vku.udn.vn/handle/123456789/2698 | - |
dc.description | Proceeding of The 12th Conference on Information Technology and It's Applications (CITA 2023); pp: 65-77. | vi_VN |
dc.description.abstract | Today, the crime rate due to the use of hot weapons is very high, early detection of possible violent situations is extremely important for security. One way to prevent these situations is by detecting the presence of dangerous objects in surveillance video. Current surveillance systems still require human supervision, are low-precision and costly. AI (Artificial Intelligence) technology is being used in almost all sectors of society. Recently, the Smart Camera application that uses Artificial Intelligence to solve complex problems is increasingly popular. This paper focuses on improving the performance of Surveillance Cameras to detect pistol in both accuracy and diversity by using YOLO -V5, V7, V8 models. | vi_VN |
dc.language.iso | en | vi_VN |
dc.publisher | Vietnam-Korea University of Information and Communication Technology | vi_VN |
dc.relation.ispartofseries | CITA; | - |
dc.subject | Hot Weapon | vi_VN |
dc.subject | Pistol Detection | vi_VN |
dc.subject | YOLO-V5 | vi_VN |
dc.subject | YOLO-V7 | vi_VN |
dc.subject | YOLO-V8 | vi_VN |
dc.title | Weapon Detection with YOLO Model Version 5, 7, 8 | vi_VN |
dc.type | Working Paper | vi_VN |
Appears in Collections: | CITA 2023 (National) |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.