Please use this identifier to cite or link to this item: https://doi.org/10.1109/ICA-SYMP50206.2021.9358449
DC FieldValue
dc.titleDrone detection using YOLOv3 with transfer learning on NVIDIA Jetson TX2
dc.contributor.authorWei Xun, DT
dc.contributor.authorLim, YL
dc.contributor.authorSrigrarom, S
dc.date.accessioned2022-03-25T06:13:54Z
dc.date.available2022-03-25T06:13:54Z
dc.date.issued2021-01-20
dc.identifier.citationWei Xun, DT, Lim, YL, Srigrarom, S (2021-01-20). Drone detection using YOLOv3 with transfer learning on NVIDIA Jetson TX2. 2021 Second International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP). ScholarBank@NUS Repository. https://doi.org/10.1109/ICA-SYMP50206.2021.9358449
dc.identifier.isbn9781728187600
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/217696
dc.description.abstractThe rise of drones in the recent years largely due to the advancements of drone technology which provide drones the ability to perform many more complex tasks autonomously with the incorporation of technologies such as computer vision, object avoidance and artificial intelligence. However, the misuse of drones such as the Gatwick Airport drone incident resulted in major disruptions which affected approximately 140,000 passengers. To deter this from happening in the future, drone surveillance are extremely crucial. With this, it will be achieved firstly by detection and followed by tracking of drones. This paper presents and investigates the use of a deep learning object detector, YOLOv3 with pretrained weights and transfer learning to train YOLOv3 to specifically detect drones. We demonstrated that the detection results from YOLOv3 after machine learning had an average accuracy of 88.9% at input image size of 416 × 416. Finally, we integrated into NVIDIA Jetson TX2 for real-time drone detection.
dc.publisherIEEE
dc.sourceElements
dc.typeConference Paper
dc.date.updated2022-03-25T04:04:21Z
dc.contributor.departmentTEMASEK LABORATORIES
dc.description.doi10.1109/ICA-SYMP50206.2021.9358449
dc.description.sourcetitle2021 Second International Symposium on Instrumentation, Control, Artificial Intelligence, and Robotics (ICA-SYMP)
dc.published.statePublished
Appears in Collections:Elements
Staff Publications

Show simple item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
YokeLin_Daniel_Spot_paper_for_ICA_SYMP2021(1).pdfPublished version2.11 MBAdobe PDF

CLOSED

None

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.