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Pregled bibliografske jedinice broj: 1120010

Automatic Person Detection in Search and Rescue Operations Using Deep CNN Detectors

Sambolek, Saša; Ivašić-Kos, Marina
Automatic Person Detection in Search and Rescue Operations Using Deep CNN Detectors // IEEE Access, 9 (2021), 37905-37922 doi:10.1109/ACCESS.2021.3063681 (međunarodna recenzija, članak, znanstveni)

CROSBI ID: 1120010 Za ispravke kontaktirajte CROSBI podršku putem web obrasca

Naslov
Automatic Person Detection in Search and Rescue Operations Using Deep CNN Detectors

Autori
Sambolek, Saša ; Ivašić-Kos, Marina

Izvornik
IEEE Access (2169-3536) 9 (2021); 37905-37922

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Convolutional neural networks ; object detector ; person detection ; search and rescue operations ; UAV ; YOLOv4 detector ; Faster R-CNN ; Cascade R-CNN ; rescue scenes ; Drones

Sažetak
Due to a growing number of people who carry out various adrenaline activities or adventure tourism and stay in the mountains and other inaccessible places, there is an increasing need to organize a search and rescue operation (SAR) to provide assistance and health care to the injured. The goal of SAR operation is to search the largest area of the territory in the shortest time possible and find a lost or injured person. Today, drones (UAVs or drones) are increasingly involved in search operations, as they can capture a large, controlled area in a short amount of time. However, a detailed examination of a large amount of recorded material remains a problem. Even for an expert, it is not easy to find searched people who are relatively small considering the area where they are, often sheltered by vegetation or merged with the ground and in unusual positions due to falls, injuries, or exhaustion. Therefore, the automatic detection of persons and objects in images/videos taken by drones in these operations is very significant. In this paper, the reliability of existing state-of-the-art detectors such as Faster R-CNN, YOLOv4, RetinaNet, and Cascade R-CNN on a VisDrone benchmark and custom- made dataset SARD build to simulate rescue scenes was investigated. After training the models on selected datasets, detection results were compared. Because of the high speed and accuracy and the small number of false detections, the YOLOv4 detector was chosen for further examination. YOLOv4 model results related to different network sizes, different detection accuracies, and transfer learning settings were analyzed. The model robustness to weather conditions and motion blur were also investigated. The paper proposes a model that can be used in SAR operations because of the excellent results in detecting people in search and rescue scenarios.

Izvorni jezik
Engleski

Znanstvena područja
Računarstvo, Informacijske i komunikacijske znanosti



POVEZANOST RADA

Projekti:
HRZZ-DOK-2018-09-8419 - Automatsko raspoznavanje akcija i aktivnosti u multimedijalnom sadržaju iz domene sporta (RAASS) (Ivašić Kos, Marina, HRZZ - 2018-09) ( CroRIS)
HRZZ-IP-2018-01-7619 - Pristup utemeljen na znanju za analizu mnoštva ljudi u nadzornim sustavima (KACAVIS) (Ribarić, Slobodan, HRZZ ) ( CroRIS)

Ustanove:
Fakultet informatike i digitalnih tehnologija, Rijeka

Profili:

Avatar Url Marina Ivašić Kos (autor)

Avatar Url Saša Sambolek (autor)

Poveznice na cjeloviti tekst rada:

Pristup cjelovitom tekstu rada doi ieeexplore.ieee.org

Citiraj ovu publikaciju:

Sambolek, Saša; Ivašić-Kos, Marina
Automatic Person Detection in Search and Rescue Operations Using Deep CNN Detectors // IEEE Access, 9 (2021), 37905-37922 doi:10.1109/ACCESS.2021.3063681 (međunarodna recenzija, članak, znanstveni)
Sambolek, S. & Ivašić-Kos, M. (2021) Automatic Person Detection in Search and Rescue Operations Using Deep CNN Detectors. IEEE Access, 9, 37905-37922 doi:10.1109/ACCESS.2021.3063681.
@article{article, author = {Sambolek, Sa\v{s}a and Iva\v{s}i\'{c}-Kos, Marina}, year = {2021}, pages = {37905-37922}, DOI = {10.1109/ACCESS.2021.3063681}, keywords = {Convolutional neural networks, object detector, person detection, search and rescue operations, UAV, YOLOv4 detector, Faster R-CNN, Cascade R-CNN, rescue scenes, Drones}, journal = {IEEE Access}, doi = {10.1109/ACCESS.2021.3063681}, volume = {9}, issn = {2169-3536}, title = {Automatic Person Detection in Search and Rescue Operations Using Deep CNN Detectors}, keyword = {Convolutional neural networks, object detector, person detection, search and rescue operations, UAV, YOLOv4 detector, Faster R-CNN, Cascade R-CNN, rescue scenes, Drones} }
@article{article, author = {Sambolek, Sa\v{s}a and Iva\v{s}i\'{c}-Kos, Marina}, year = {2021}, pages = {37905-37922}, DOI = {10.1109/ACCESS.2021.3063681}, keywords = {Convolutional neural networks, object detector, person detection, search and rescue operations, UAV, YOLOv4 detector, Faster R-CNN, Cascade R-CNN, rescue scenes, Drones}, journal = {IEEE Access}, doi = {10.1109/ACCESS.2021.3063681}, volume = {9}, issn = {2169-3536}, title = {Automatic Person Detection in Search and Rescue Operations Using Deep CNN Detectors}, keyword = {Convolutional neural networks, object detector, person detection, search and rescue operations, UAV, YOLOv4 detector, Faster R-CNN, Cascade R-CNN, rescue scenes, Drones} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus

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