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Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images

Saeed Masoud Alshahrani1, Saud S. Alotaibi2, Shaha Al-Otaibi3, Mohamed Mousa4, Anwer Mustafa Hilal5,*, Amgad Atta Abdelmageed5, Abdelwahed Motwakel5, Mohamed I. Eldesouki6

1 Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, Saudi Arabia
2 Department of Information Systems, College of Computing and Information System, Umm Al-Qura University, Saudi Arabia
3 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia
4 Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo, 11845, Egypt
5 Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia
6 Department of Information System, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, AlKharj, Saudi Arabia

* Corresponding Author: Anwer Mustafa Hilal. Email: email

Computers, Materials & Continua 2023, 74(2), 3117-3131. https://doi.org/10.32604/cmc.2023.033038

Abstract

Object detection (OD) in remote sensing images (RSI) acts as a vital part in numerous civilian and military application areas, like urban planning, geographic information system (GIS), and search and rescue functions. Vehicle recognition from RSIs remained a challenging process because of the difficulty of background data and the redundancy of recognition regions. The latest advancements in deep learning (DL) approaches permit the design of effectual OD approaches. This study develops an Artificial Ecosystem Optimizer with Deep Convolutional Neural Network for Vehicle Detection (AEODCNN-VD) model on Remote Sensing Images. The proposed AEODCNN-VD model focuses on the identification of vehicles accurately and rapidly. To detect vehicles, the presented AEODCNN-VD model employs single shot detector (SSD) with Inception network as a baseline model. In addition, Multiway Feature Pyramid Network (MFPN) is used for handling objects of varying sizes in RSIs. The features from the Inception model are passed into the MFPN for multiway and multiscale feature fusion. Finally, the fused features are passed into bounding box and class prediction networks. For enhancing the detection efficiency of the AEODCNN-VD approach, AEO based hyperparameter optimizer is used, which is stimulated by the energy transfer strategies such as production, consumption, and decomposition in an ecosystem. The performance validation of the presented method on benchmark datasets showed promising performance over recent DL models.

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APA Style
Alshahrani, S.M., Alotaibi, S.S., Al-Otaibi, S., Mousa, M., Hilal, A.M. et al. (2023). Optimal deep convolutional neural network for vehicle detection in remote sensing images. Computers, Materials & Continua, 74(2), 3117-3131. https://doi.org/10.32604/cmc.2023.033038
Vancouver Style
Alshahrani SM, Alotaibi SS, Al-Otaibi S, Mousa M, Hilal AM, Abdelmageed AA, et al. Optimal deep convolutional neural network for vehicle detection in remote sensing images. Comput Mater Contin. 2023;74(2):3117-3131 https://doi.org/10.32604/cmc.2023.033038
IEEE Style
S.M. Alshahrani et al., "Optimal Deep Convolutional Neural Network for Vehicle Detection in Remote Sensing Images," Comput. Mater. Contin., vol. 74, no. 2, pp. 3117-3131. 2023. https://doi.org/10.32604/cmc.2023.033038



cc 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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