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Efficient Deep Learning Modalities for Object Detection from Infrared Images

Naglaa F. Soliman1,2, E. A. Alabdulkreem3, Abeer D. Algarni1,*, Ghada M. El Banby4, Fathi E. Abd El-Samie1,5, Ahmed Sedik6

1 Department of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 84428, Saudi Arabia
2 Department of Electronics and Communications, Faculty of Engineering, Zagazig University, Zagazig, 44519, Egypt
3 Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh, 84428, Saudi Arabia
4 Department of Industrial Electronics and Control Engineering, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt
5 Department of Electronics and Electrical Communications, Faculty of Electronic Engineering, Menoufia University, Menouf, 32952, Egypt
6 Department of the Robotics and Intelligent Machines, Faculty of Artificial Intelligence, KafrelSheikh University, Kafr el-sheikh, Egypt

* Corresponding Author: Abeer D. Algarni. Email: email

Computers, Materials & Continua 2022, 72(2), 2545-2563. https://doi.org/10.32604/cmc.2022.020107

Abstract

For military warfare purposes, it is necessary to identify the type of a certain weapon through video stream tracking based on infrared (IR) video frames. Computer vision is a visual search trend that is used to identify objects in images or video frames. For military applications, drones take a main role in surveillance tasks, but they cannot be confident for long-time missions. So, there is a need for such a system, which provides a continuous surveillance task to support the drone mission. Such a system can be called a Hybrid Surveillance System (HSS). This system is based on a distributed network of wireless sensors for continuous surveillance. In addition, it includes one or more drones to make short-time missions, if the sensors detect a suspicious event. This paper presents a digital solution to identify certain types of concealed weapons in surveillance applications based on Convolutional Neural Networks (CNNs) and Convolutional Long Short-Term Memory (ConvLSTM). Based on initial results, the importance of video frame enhancement is obvious to improve the visibility of objects in video streams. The accuracy of the proposed methods reach 99%, which reflects the effectiveness of the presented solution. In addition, the experimental results prove that the proposed methods provide superior performance compared to traditional ones.

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Cite This Article

APA Style
Soliman, N.F., Alabdulkreem, E.A., Algarni, A.D., Banby, G.M.E., El-Samie, F.E.A. et al. (2022). Efficient deep learning modalities for object detection from infrared images. Computers, Materials & Continua, 72(2), 2545-2563. https://doi.org/10.32604/cmc.2022.020107
Vancouver Style
Soliman NF, Alabdulkreem EA, Algarni AD, Banby GME, El-Samie FEA, Sedik A. Efficient deep learning modalities for object detection from infrared images. Comput Mater Contin. 2022;72(2):2545-2563 https://doi.org/10.32604/cmc.2022.020107
IEEE Style
N.F. Soliman, E.A. Alabdulkreem, A.D. Algarni, G.M.E. Banby, F.E.A. El-Samie, and A. Sedik, “Efficient Deep Learning Modalities for Object Detection from Infrared Images,” Comput. Mater. Contin., vol. 72, no. 2, pp. 2545-2563, 2022. https://doi.org/10.32604/cmc.2022.020107



cc Copyright © 2022 The Author(s). Published by Tech Science Press.
This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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