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Article

Automatic Handgun Detection with Deep Learning in Video Surveillance Images

1
Department of Electrical, Electronic, Automatic and Communications Engineering—IEEAC, School of Computer Science, University of Castilla-La Mancha, Paseo de la Universidad 4, 13071 Ciudad Real, Spain
2
Department of Electrical, Electronic, Automatic and Communications Engineering—IEEAC, Higher Technical School of Industrial Engineering, University of Castilla-La Mancha, Avenida de Camilo José Cela s/n, 13071 Ciudad Real, Spain
*
Author to whom correspondence should be addressed.
Academic Editor: Pierluigi Siano
Appl. Sci. 2021, 11(13), 6085; https://doi.org/10.3390/app11136085
Received: 26 May 2021 / Revised: 24 June 2021 / Accepted: 28 June 2021 / Published: 30 June 2021
There is a great need to implement preventive mechanisms against shootings and terrorist acts in public spaces with a large influx of people. While surveillance cameras have become common, the need for monitoring 24/7 and real-time response requires automatic detection methods. This paper presents a study based on three convolutional neural network (CNN) models applied to the automatic detection of handguns in video surveillance images. It aims to investigate the reduction of false positives by including pose information associated with the way the handguns are held in the images belonging to the training dataset. The results highlighted the best average precision (96.36%) and recall (97.23%) obtained by RetinaNet fine-tuned with the unfrozen ResNet-50 backbone and the best precision (96.23%) and F1 score values (93.36%) obtained by YOLOv3 when it was trained on the dataset including pose information. This last architecture was the only one that showed a consistent improvement—around 2%—when pose information was expressly considered during training. View Full-Text
Keywords: weapon detection; gun detection; computer vision; deep learning; building automation; terrorism weapon detection; gun detection; computer vision; deep learning; building automation; terrorism
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MDPI and ACS Style

Salido, J.; Lomas, V.; Ruiz-Santaquiteria, J.; Deniz, O. Automatic Handgun Detection with Deep Learning in Video Surveillance Images. Appl. Sci. 2021, 11, 6085. https://doi.org/10.3390/app11136085

AMA Style

Salido J, Lomas V, Ruiz-Santaquiteria J, Deniz O. Automatic Handgun Detection with Deep Learning in Video Surveillance Images. Applied Sciences. 2021; 11(13):6085. https://doi.org/10.3390/app11136085

Chicago/Turabian Style

Salido, Jesus, Vanesa Lomas, Jesus Ruiz-Santaquiteria, and Oscar Deniz. 2021. "Automatic Handgun Detection with Deep Learning in Video Surveillance Images" Applied Sciences 11, no. 13: 6085. https://doi.org/10.3390/app11136085

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