<link rel="stylesheet" href="styles.f3b1fba60ec7970c.css">

Drone detection and classification using deep leaning

dc.contributor.authorBENHAFRI, MERIEM
dc.contributor.authorCHARRIF, MERIEM
dc.contributor.authorKHELDOUN, AHMED
dc.date.accessioned2024-10-21T13:44:47Z
dc.date.available2024-10-21T13:44:47Z
dc.date.issued2024
dc.description.abstractThis document explores the application of deep learning models, specifically YOLOv5 and YOLOv8, for drone detection, with a focus on addressing the challenge of detecting small drones. Through rigorous experimentation and optimization of training parameters, our study aims to enhance the accuracy and efficiency of drone detection systems. The results demonstrate significant benefits, particularly in real-time performance and precision, crucial for enhancing security and surveillance capabilities in various environments.fr_FR
dc.identifier.citationIngénierie des systèmes et technologie du webfr_FR
dc.identifier.govdocM62496
dc.identifier.urihttps://dspace.univ-medea.dz/handle/123456789/12170
dc.language.isoenfr_FR
dc.relation.ispartofseriesM62496;
dc.subjectdrone detectionfr_FR
dc.subjectDeep learningfr_FR
dc.subjectYOLOv5fr_FR
dc.subjectYOLOv8fr_FR
dc.subjectsmall dronesfr_FR
dc.titleDrone detection and classification using deep leaningfr_FR
dc.typeMemoirefr_FR

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Thumbnail Image
Name:
drone detection and classification using deep learning.pdf
Size:
10.37 MB
Format:
Adobe Portable Document Format
Description: