Sangam: A Confluence of Knowledge Streams

Deep Learning based Vehicle Detection in Aerial Imagery

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dc.creator Wilko Sommer, Lars
dc.date 2022-03-01T04:02:30Z
dc.date 2022-03-01T04:02:30Z
dc.date 2022-02-28T14:31:05Z
dc.date 2022
dc.date.accessioned 2023-02-18T19:29:46Z
dc.date.available 2023-02-18T19:29:46Z
dc.identifier ONIX_20220228_9783731511137_3
dc.identifier 1863-6489
dc.identifier https://library.oapen.org/handle/20.500.12657/53149
dc.identifier https://directory.doabooks.org/handle/20.500.12854/78910
dc.identifier https://library.oapen.org/bitstream/20.500.12657/53149/1/9783731511137.pdf
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/249688
dc.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.
dc.format image/jpeg
dc.language eng
dc.publisher KIT Scientific Publishing
dc.publisher KIT Scientific Publishing
dc.relation Karlsruher Schriften zur Anthropomatik
dc.rights open access
dc.subject Objektdetektion
dc.subject Neuronale Netze
dc.subject Luftbilddaten
dc.subject Object Detection
dc.subject Deep Learning
dc.subject Aerial Imagery
dc.subject bic Book Industry Communication::U Computing & information technology::UY Computer science::UYA Mathematical theory of computation::UYAM Maths for computer scientists
dc.title Deep Learning based Vehicle Detection in Aerial Imagery
dc.resourceType book
dc.alternateIdentifier 9783731511137
dc.alternateIdentifier 10.5445/KSP/1000135415
dc.licenseCondition open access
dc.licenseCondition n/a
dc.identifierdoi 10.5445/KSP/1000135415
dc.relationisPublishedBy 68fffc18-8f7b-44fa-ac7e-0b7d7d979bd2
dc.relationisbn 9783731511137
dc.pages 276
dc.placepublication Karlsruhe
dc.imprint KIT Scientific Publishing


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