The automatic detection of underage troopers from live-videos based on Deep Learning

• Autorzy: Waisi Najwan Zuhair , Abdullah Nawal Younis , Ghazal Mohammed Tala

Identyfikatory

DOI

10.15199/48.2021.09.18

Warianty tytułu

PL

Automatyczne wykrywanie nieletnich żołnierzy z wideo na żywo w oparciu o metodę Deep Learning

• Języki publikacji: EN

Abstrakty

EN

Military service is undoubtedly among the most profound forms of service to the nation. With military service young people can develop qualities of discipline within them, but nobody should be forced to serve, and especially young children. A real-time Child Troopers detection surveillance system is built to overcome these bad acts, based on Convolutional Neural Networks (CNNs). This method is focused on the automatic face, age, and weapon detection. The proposed detection and identification system consist of many steps of process: starting with, a pre-trained deep learning model based on SSD-ResNet network to perform face detection operation. Then, an age estimation using VGG-Face model is performed, finally, a weapon detection based on MobileNetV2-SSD pretrained model. The results of these steps are combined to look for children under 18 years old with guns in the images. These models have been selected because of there fast and accurate in infering to integrate network for detecting and identifying children with weapons in images. The experimental result using global datasets of various images for faces and weapons showed that the use of this method enhances the accuracy level of detection.

PL

Dzięki służbie wojskowej młodzi ludzie mogą rozwinąć w sobie cechy dyscypliny, ale nikt nie powinien być zmuszany do służby, a zwłaszcza małe dzieci. Zaproponowano jest system nadzoru wykrywający w czasie rzeczywistym Child Troopers, oparty na Convolutional Neural Networks (CNN). Ta metoda skupia się na automatycznym wykrywaniu twarzy, wieku i broni. Proponowany system detekcji i identyfikacji składa się z wielu etapów procesu: zaczynając od wstępnie wytrenowanego modelu głębokiego uczenia opartego na sieci SSD-ResNet do wykonywania operacji wykrywania twarzy. Następnie przeprowadzana jest estymacja wieku za pomocą modelu VGG-Face, a na koniec detekcja broni w oparciu o wstępnie wytrenowany model MobileNetV2-SSD. Wyniki tych kroków są łączone w celu wyszukania na zdjęciach dzieci poniżej 18 roku życia z bronią. Modele te zostały wybrane ze względu na szybkie i dokładne wnioskowanie do integracji sieci do wykrywania i identyfikacji dzieci z bronią na obrazach. Wyniki eksperymentalne wykorzystujące globalne zbiory danych różnych obrazów twarzy i broni wykazały, że zastosowanie tej metody zwiększa poziom dokładności wykrywania

Słowa kluczowe

EN

deep learning; convolutional neural network; CNN; child trooper; VGG-Fac; SSD-ResNet-101; MobileNetV2-SSD

PL

deep lerning; wykrywanie twarzy; identyfikacja twarzy; wykrywanie nieletnich

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2021
• Tom: R. 97, nr 9
• Strony: 85--88
• Opis fizyczny: Bibliogr. 28 poz., rys., tab.

Twórcy

autor

Waisi Najwan Zuhair

• Northern Technical University, Iraq

• https://orcid.org/0000-0003-4259-1294

autor

Abdullah Nawal Younis

• Northern Technical University, Iraq

• https://orcid.org/0000-0003-1217-4959

autor

Ghazal Mohammed Tala

• Northern Technical University, Iraq

• https://orcid.org/0000-0003-3159-4983

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).