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Detection of Object (Weapons) With Deep Learning Algorithms From İmages Obtained By Unmanned Aerial Vehicles

Yıl 2022, Cilt 13, Sayı 2, 263 - 270, 28.06.2022
https://doi.org/10.24012/dumf.1116534

Öz

Today, the rapid development of Artificial Intelligence technologies is effective in the success of deep learning algorithms in different application areas. In addition to these applications, it detects many objects that cannot be noticed even with the human eye in object detection in video and images with deep learning algorithms. In this study, it was aimed to detect weapons by using images obtained from UAV using deep learning algorithms. Regional Based Convolutional Neural Networks and Residual Network were used and the performance evaluation of the architecture of these algorithms was presented. Performance evaluation of algorithms was made using Loss plots, Precision-Recall, and mAP curves. In this study, 200 images of different angles and heights were obtained from the unmanned aerial vehicle. Two-thirds of the images we obtained were divided into training and one-third test images. Resnet and Region Based Convolutional neural networks used in test images have been successful in object detection. Regional based neural networks and Residual Network that we use for object detection are used. Images from different angles and heights obtained from the unmanned aerial vehicle are trained using regional-based neural networks and residual network algorithms. 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, the desired image detection was detected at a rate of 99%. The performance evaluation of the detected images is discussed and the success of the deep learning algorithms used in object detection is presented. Considered in the performance evaluation of deep learning algorithms, Loss Charts (Error), Precision-Recall curves show the accuracy of detection in a short time. Algorithms that will increase the possibilities and capabilities of unmanned aerial vehicles used especially in border security and internal security are considered to increase in the future.

Kaynakça

  • C., Budak, M. Türk and A. Toprak, “Reduction in impulse noise in digital images through a new adaptive artificial neural network model”, Neural Comput & Applic, vol. 26, no.4, pp. 835–843, 2015.

Derin öğrenme Algoritmalarıyla İnsansız Hava Araçlarından Elde Edilen Görüntülerde Nesne (Silah) Tespiti

Yıl 2022, Cilt 13, Sayı 2, 263 - 270, 28.06.2022
https://doi.org/10.24012/dumf.1116534

Öz

Today, the rapid development of Artificial Intelligence technologies is effective in the success of deep learning algorithms in different application areas. In addition to these applications, it detects many objects that cannot be noticed even with the human eye in object detection in video and images with deep learning algorithms. In this study, it was aimed to detect weapons by using images obtained from UAV using deep learning algorithms. Regional Based Convolutional Neural Networks and Residual Network were used and the performance evaluation of the architecture of these algorithms was presented. Performance evaluation of algorithms was made using Loss plots, Precision-Recall, and mAP curves. In this study, 200 images of different angles and heights were obtained from the unmanned aerial vehicle. Two-thirds of the images we obtained were divided into training and one-third test images. Resnet and Region Based Convolutional neural networks used in test images have been successful in object detection. Regional based neural networks and Residual Network that we use for object detection are used. Images from different angles and heights obtained from the unmanned aerial vehicle are trained using regional-based neural networks and residual network algorithms. 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, the desired image detection was detected at a rate of 99%. The performance evaluation of the detected images is discussed and the success of the deep learning algorithms used in object detection is presented. Considered in the performance evaluation of deep learning algorithms, Loss Charts (Error), Precision-Recall curves show the accuracy of detection in a short time. Algorithms that will increase the possibilities and capabilities of unmanned aerial vehicles used especially in border security and internal security are considered to increase in the future.

Kaynakça

  • C., Budak, M. Türk and A. Toprak, “Reduction in impulse noise in digital images through a new adaptive artificial neural network model”, Neural Comput & Applic, vol. 26, no.4, pp. 835–843, 2015.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik, Ortak Disiplinler
Bölüm Makaleler
Yazarlar

Mustafa BURGAZ> (Sorumlu Yazar)
TSK
0000-0001-7525-2649
Türkiye


Cafer BUDAK>
DİCLE ÜNİVERSİTESİ, MÜHENDİSLİK FAKÜLTESİ
0000-0002-8470-4579
Türkiye

Erken Görünüm Tarihi 28 Haziran 2022
Yayımlanma Tarihi 28 Haziran 2022
Yayınlandığı Sayı Yıl 2022, Cilt 13, Sayı 2

Kaynak Göster

IEEE M. Burgaz ve C. Budak , "Detection of Object (Weapons) With Deep Learning Algorithms From İmages Obtained By Unmanned Aerial Vehicles", Dicle Üniversitesi Mühendislik Fakültesi Mühendislik Dergisi, c. 13, sayı. 2, ss. 263-270, Haz. 2022, doi:10.24012/dumf.1116534
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