Deep Learning based Vehicle Detection in Aerial Imagery
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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Electronic Book Chapter |
| Language: | English |
| Published: |
Karlsruhe
KIT Scientific Publishing
2022
|
| Series: | Karlsruher Schriften zur Anthropomatik
|
| Subjects: | |
| Online Access: | DOAB: download the publication DOAB: description of the publication |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
MARC
| LEADER | 00000naaaa2200000uu 4500 | ||
|---|---|---|---|
| 001 | doab_20_500_12854_78910 | ||
| 005 | 20220301 | ||
| 003 | oapen | ||
| 006 | m o d | ||
| 007 | cr|mn|---annan | ||
| 008 | 20220301s2022 xx |||||o ||| 0|eng d | ||
| 020 | |a KSP/1000135415 | ||
| 020 | |a 9783731511137 | ||
| 040 | |a oapen |c oapen | ||
| 024 | 7 | |a 10.5445/KSP/1000135415 |c doi | |
| 041 | 0 | |a eng | |
| 042 | |a dc | ||
| 072 | 7 | |a UYAM |2 bicssc | |
| 100 | 1 | |a Wilko Sommer, Lars |4 auth | |
| 245 | 1 | 0 | |a Deep Learning based Vehicle Detection in Aerial Imagery |
| 260 | |a Karlsruhe |b KIT Scientific Publishing |c 2022 | ||
| 300 | |a 1 electronic resource (276 p.) | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 490 | 1 | |a Karlsruher Schriften zur Anthropomatik | |
| 506 | 0 | |a Open Access |2 star |f Unrestricted online access | |
| 520 | |a 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. | ||
| 540 | |a Creative Commons |f by-sa/4.0 |2 cc |4 http://creativecommons.org/licenses/by-sa/4.0 | ||
| 546 | |a English | ||
| 650 | 7 | |a Maths for computer scientists |2 bicssc | |
| 653 | |a Objektdetektion | ||
| 653 | |a Neuronale Netze | ||
| 653 | |a Luftbilddaten | ||
| 653 | |a Object Detection | ||
| 653 | |a Deep Learning | ||
| 653 | |a Aerial Imagery | ||
| 856 | 4 | 0 | |a www.oapen.org |u https://library.oapen.org/bitstream/20.500.12657/53149/1/9783731511137.pdf |7 0 |z DOAB: download the publication |
| 856 | 4 | 0 | |a www.oapen.org |u https://directory.doabooks.org/handle/20.500.12854/78910 |7 0 |z DOAB: description of the publication |