Detection of object (Weapons) with deep learning algorithms from images obtained by unmanned aerial vehicles

Basit Öğe Kaydı
dc.authorid0000-0001-7525-2649en_US
dc.authorid0000-0002-8470-4579en_US
dc.contributor.authorBurgaz, Mustafa
dc.contributor.authorBudak, Cafer
dc.date.accessioned2022-07-05T12:46:35Z
dc.date.available2022-07-05T12:46:35Z
dc.date.issued2022en_US
dc.departmentDicle Üniversitesi, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.description.abstractToday, the rapid development of Artificial Intelligence technologies is effective in the success of deep learning algorithms in different application areas. These applications detect many objects that even the human eye cannot detect in object detection in videos and images with deep learning algorithms.In this study, it is aimed to detect weapons from images obtained from Unmanned Aerial Vehicle (UAV) by using deep learning algorithms. Images were obtained from the UAV at 200 different angles and heights. Images from different angles and heights obtained from the unmanned aerial vehicle are trained by Regional Based Convolutional Neural Networks (R-CNN) and Residual Neural Network (ResNet). Twothirds of the images we obtained were split into training images and one-third into test images. The feature maps extracted from the images used for training were compared with the test images. By bringing these compared images closer to the desired images, 99% of the desired image detection is achieved. Performance evaluation of the algorithms was made using Loss plot, mAP curves, Precision, Recall and F1-Score. The performance evaluation of the detected images is discussed, and the success of deep learning algorithms used in object detection is presented. The ResNet model showed higher performance with 64% accuracy, 94% recall and 76% F1 score.en_US
dc.identifier.citationBurgaz, M. ve Budak, C. (2022). Detection of object (Weapons) with deep learning algorithms from images obtained by unmanned aerial vehicles. Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, 13(2), 263-270.en_US
dc.identifier.doi10.24012/dumf.1116534
dc.identifier.endpage270en_US
dc.identifier.issn1309-8640
dc.identifier.issn2146-4391
dc.identifier.issue2en_US
dc.identifier.startpage263en_US
dc.identifier.urihttps://dergipark.org.tr/tr/download/article-file/2427220
dc.identifier.urihttps://hdl.handle.net/11468/10117
dc.identifier.volume13en_US
dc.institutionauthorBudak, Cafer
dc.language.isoenen_US
dc.publisherDicle Üniversitesi Mühendislik Fakültesien_US
dc.relation.ispartofDicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi
dc.relation.publicationcategoryMakale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectDeep learningen_US
dc.subjectUnmanned aerial vehiclesen_US
dc.subjectRegional based convolutional neural networksen_US
dc.subjectResNeten_US
dc.titleDetection of object (Weapons) with deep learning algorithms from images obtained by unmanned aerial vehiclesen_US
dc.titleDetection of object (Weapons) with deep learning algorithms from images obtained by unmanned aerial vehicles
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Detection of Object (Weapons) with Deep Learning Algorithms from Images.pdf
Boyut:
879.6 KB
Biçim:
Adobe Portable Document Format
Açıklama:
Makale Dosyası
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
[ X ]
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Dicle Üniversitesi Mühendislik Fakültesi Dergisi