DRONET: Multi-Tasking Framework for Real-Time Industrial Facility Aerial Surveillance and Safety
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ajakwe, Simeon Okechukwu | - |
dc.contributor.author | Ihekoronye, Vivian Ukamaka | - |
dc.contributor.author | Kim, Dong-Seong | - |
dc.contributor.author | Lee, Jae Min | - |
dc.date.accessioned | 2022-05-02T08:40:06Z | - |
dc.date.available | 2022-05-02T08:40:06Z | - |
dc.date.created | 2022-04-25 | - |
dc.date.issued | 2022-02 | - |
dc.identifier.issn | 2504-446X | - |
dc.identifier.uri | https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/21031 | - |
dc.description.abstract | The security of key and critical infrastructures is crucial for uninterrupted industrial process flow needed in strategic management as these facilities are major targets of invaders. The emergence of non-military use of drones especially for logistics comes with the challenge of redefining the anti-drone approach in determining a drone's harmful status in the airspace based on certain metrics before countering it. In this work, a vision-based multi-tasking anti-drone framework is proposed to detect drones, identifies the airborne objects, determines its harmful status through perceived threat analysis, and checks its proximity in real-time prior to taking an action. The model is validated using manually generated 5460 drone samples from six (6) drone models under sunny, cloudy, and evening scenarios and 1709 airborne objects samples of seven (7) classes under different environments, scenarios (blur, scales, low illumination), and heights. The proposed model was compared with seven (7) other object detection models in terms of accuracy, sensitivity, F1-score, latency, throughput, reliability, and efficiency. The simulation result reveals that, overall, the proposed model achieved superior multi-drone detection accuracy of 99.6%, attached object identification of sensitivity of 99.80%, and F1-score of 99.69%, with minimal error, low latency, and less computational complexity needed for effective industrial facility aerial surveillance. A benchmark dataset is also provided for subsequent performance evaluation of other object detection models. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | MDPI | - |
dc.title | DRONET: Multi-Tasking Framework for Real-Time Industrial Facility Aerial Surveillance and Safety | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Ajakwe, Simeon Okechukwu | - |
dc.contributor.affiliatedAuthor | Ihekoronye, Vivian Ukamaka | - |
dc.contributor.affiliatedAuthor | Kim, Dong-Seong | - |
dc.contributor.affiliatedAuthor | Lee, Jae Min | - |
dc.identifier.doi | 10.3390/drones6020046 | - |
dc.identifier.wosid | 000770893500001 | - |
dc.identifier.bibliographicCitation | DRONES, v.6, no.2 | - |
dc.relation.isPartOf | DRONES | - |
dc.citation.title | DRONES | - |
dc.citation.volume | 6 | - |
dc.citation.number | 2 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | Y | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Remote Sensing | - |
dc.relation.journalWebOfScienceCategory | Remote Sensing | - |
dc.subject.keywordPlus | FEATURES | - |
dc.subject.keywordAuthor | aerial surveillance | - |
dc.subject.keywordAuthor | anti-drone communication | - |
dc.subject.keywordAuthor | drone detection | - |
dc.subject.keywordAuthor | deep learning | - |
dc.subject.keywordAuthor | facility | - |
dc.subject.keywordAuthor | security | - |
dc.subject.keywordAuthor | weapons | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
350-27, Gumi-daero, Gumi-si, Gyeongsangbuk-do, Republic of Korea (39253)054-478-7170
COPYRIGHT 2020 Kumoh University All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.