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Saving Lives from Above: Person Detection in Disaster Response Using Deep Neural Networks

Bahmanyar, Reza und Merkle, Nina (2023) Saving Lives from Above: Person Detection in Disaster Response Using Deep Neural Networks. In: ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Seiten 1-8. ISPRS Geospatial Week, 2023-09-02 - 2023-09-07, Cairo, Egypt. doi: 10.5194/isprs-annals-X-1-W1-2023-343-2023. ISSN 2194-9042.

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Kurzfassung

This paper focuses on person detection in aerial and drone imagery, which is crucial for various operations such as situational awareness, search and rescue, and safe delivery of supplies. We aim to improve disaster response efforts by enhancing the speed, safety, and effectiveness of the process. Therefore, we introduce a new person detection dataset comprising 311 annotated aerial and drone images, acquired from helicopters and drones in different scenes, including urban and rural areas, and for different scenarios, such as estimation of damage in disaster-affected zones, and search and rescue operations in different countries. The amount of data considered and level of detail of the annotations resulted in a total of 10,050 annotated persons. To detect people in aerial and drone images, we propose a multi-stage training procedure to improve YOLOv3's ability. The proposed procedure aims at addressing challenges such as variations in scenes, scenarios, people poses, as well as image scales and viewing angles. To evaluate the effectiveness of our proposed training procedure, we split our dataset into a training and a test set. The latter includes images acquired during real search and rescue exercises and operations, and is therefore representative for the challenges encountered during operational missions and suitable for an accurate assessment of the proposed models. Experimental results demonstrate the effectiveness of our proposed training procedure, as the model's average precision exhibits a relevant increase with respect to the baseline value.

elib-URL des Eintrags:https://elib.dlr.de/197394/
Dokumentart:Konferenzbeitrag (Vortrag)
Titel:Saving Lives from Above: Person Detection in Disaster Response Using Deep Neural Networks
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Bahmanyar, Rezareza.bahmanyar (at) dlr.dehttps://orcid.org/0000-0002-6999-714X144621062
Merkle, NinaNina.Merkle (at) dlr.dehttps://orcid.org/0000-0003-4177-1066NICHT SPEZIFIZIERT
Datum:2023
Erschienen in:ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Nein
In SCOPUS:Ja
In ISI Web of Science:Ja
DOI:10.5194/isprs-annals-X-1-W1-2023-343-2023
Seitenbereich:Seiten 1-8
ISSN:2194-9042
Status:veröffentlicht
Stichwörter:Aerial Imagery, Deep Neural Networks, Drone Imagery, Person Detection, Small Object Detection, UAVs
Veranstaltungstitel:ISPRS Geospatial Week
Veranstaltungsort:Cairo, Egypt
Veranstaltungsart:internationale Konferenz
Veranstaltungsbeginn:2 September 2023
Veranstaltungsende:7 September 2023
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Verkehr
HGF - Programmthema:Straßenverkehr
DLR - Schwerpunkt:Verkehr
DLR - Forschungsgebiet:V ST Straßenverkehr
DLR - Teilgebiet (Projekt, Vorhaben):V - KoKoVI - Koordinierter kooperativer Verkehr mit verteilter, lernender Intelligenz
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Photogrammetrie und Bildanalyse
Hinterlegt von: Bahmanyar, Gholamreza
Hinterlegt am:17 Okt 2023 14:17
Letzte Änderung:24 Apr 2024 20:57

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