9783731511137.pdf

9783731511137.pdf

This book proposes a novel deep learning based detection method, focusing on vehicle detection in aerial imagery recorded in top view. The base detection framework is extended by two novel components to improve the detection accuracy by enhancing the contextual and semantical content of the employed...

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Γλώσσα:English
Έκδοση: KIT Scientific Publishing 2022
Διαθέσιμο Online:https://doi.org/10.5445/KSP/1000135415
id oapen-20.500.12657-53149
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spelling oapen-20.500.12657-531492022-03-01T02:49:56Z Deep Learning based Vehicle Detection in Aerial Imagery Wilko Sommer, Lars Objektdetektion Neuronale Netze Luftbilddaten Object Detection Deep Learning Aerial Imagery bic Book Industry Communication::U Computing & information technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists This book proposes a novel deep learning based detection method, focusing on vehicle detection in aerial imagery recorded in top view. The base detection framework is extended by two novel components to improve the detection accuracy by enhancing the contextual and semantical content of the employed feature representation. To reduce the inference time, a lightweight CNN architecture is proposed as base architecture and a novel module that restricts the search area is introduced. 2022-02-28T14:31:05Z 2022-02-28T14:31:05Z 2022 book ONIX_20220228_9783731511137_3 1863-6489 9783731511137 https://library.oapen.org/handle/20.500.12657/53149 eng Karlsruher Schriften zur Anthropomatik application/pdf n/a 9783731511137.pdf https://doi.org/10.5445/KSP/1000135415 KIT Scientific Publishing KIT Scientific Publishing 10.5445/KSP/1000135415 10.5445/KSP/1000135415 44e29711-8d53-496b-85cc-3d10c9469be9 9783731511137 KIT Scientific Publishing 52 276 Karlsruhe open access
institution OAPEN
collection DSpace
language English
description This book proposes a novel deep learning based detection method, focusing on vehicle detection in aerial imagery recorded in top view. The base detection framework is extended by two novel components to improve the detection accuracy by enhancing the contextual and semantical content of the employed feature representation. To reduce the inference time, a lightweight CNN architecture is proposed as base architecture and a novel module that restricts the search area is introduced.
title 9783731511137.pdf
spellingShingle 9783731511137.pdf
title_short 9783731511137.pdf
title_full 9783731511137.pdf
title_fullStr 9783731511137.pdf
title_full_unstemmed 9783731511137.pdf
title_sort 9783731511137.pdf
publisher KIT Scientific Publishing
publishDate 2022
url https://doi.org/10.5445/KSP/1000135415
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