Lidar Camera Imaging for Detection of UAVs
dc.contributor.advisor | Linga Reddy Cenkeramaddi | |
dc.contributor.author | Perhat Adiljan | |
dc.date.accessioned | 2022-09-21T16:24:43Z | |
dc.date.available | 2022-09-21T16:24:43Z | |
dc.date.issued | 2022 | |
dc.identifier | no.uia:inspera:106884834:7941736 | |
dc.identifier.uri | https://hdl.handle.net/11250/3020379 | |
dc.description | Full text not available | |
dc.description.abstract | Resent years, deep (CNN) convolutional neural network have made great strides with the development of advent of big data and image processing hardware. The method used in the experiment is YOLOv5. In the object detection area, YOLO and Faster R-CNN, those are the most popular approaches that are anchor based. | |
dc.description.abstract | ||
dc.language | ||
dc.publisher | University of Agder | |
dc.title | Lidar Camera Imaging for Detection of UAVs | |
dc.type | Master thesis |
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Master's theses in Information and Communication Technology [491]
MM500, IKT590, IKT591