High-Speed Drone Detection Based On Yolo-V8

Jun Hwa Kim, Namho Kim, Chee Sun Won

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

64 Scopus citations

Abstract

Detecting drones in a video is a challenging problem due to their dynamic movements and varying range of scales. Moreover, since drone detection is often required for security, it should be as fast as possible. In this paper, we modify the state-of-the-art YOLO-V8 to achieve fast and reliable drone detection. Specifically, we add Multi-Scale Image Fusion and P2 Layer to the medium-size model (M-model) of YOLO-V8. Our model was evaluated in the 6th WOSDETC challenge.

Original languageEnglish
Title of host publicationICASSP 2023 - 2023 IEEE International Conference on Acoustics, Speech and Signal Processing, Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728163277
DOIs
StatePublished - 2023
Event48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023 - Rhodes Island, Greece
Duration: 4 Jun 202310 Jun 2023

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume2023-June
ISSN (Print)1520-6149

Conference

Conference48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023
Country/TerritoryGreece
CityRhodes Island
Period4/06/2310/06/23

Keywords

  • Drone Detection
  • Small-object detection
  • YOLO-V8

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