Building an image processing program for the vehicle fire control system
Email:
vuxuantung0511@gmail.com
Từ khóa:
Board Jetson AI, Orange Pi 5, Kernelized Correlation Filters, Temple Matching, Linear Correlation Filter
Tóm tắt
In the world, the integration of controlled weapons into combat vehicles has been done for a long time and many weapon manufacturers have utilized image processing software to enhance the combat effectiveness of weapon systems, resulting in positive outcomes. Fire control systems, especially fire control systems on vehicles, require requirements for processing speed, durability as well as flexibility, which are essential when fighting the enemy. Kernelized Correlation Filters image processing algorithms and Temple Matching algorithms have promoted the advantages of image processing with vehicle fire control system in the weapons field. In this article, from the analysis of the Kernelized Correlation Filters image processing algorithm and the Temple Matching algorithm on hardware platforms suitable for vehicle fire control systems, the authors built a software program based on taking advantage of the powerful parallel computing capabilities of GPUs applied to 12.7 mm gun fire control systems installed on vehicles. The experiments demonstrated the results of handling targets in the field after completely installing all components of the weapon complex on the vehicleTài liệu tham khảo
[1]. S. J. Lee, Challenges of Real-time Processing with Embedded Vision for IoT Applications, in 2022 International Conference on Electrical, Computer, Communications and Mechatronics Engineering (ICECCME), (2022) 1–6. https://doi.org/10.1109/ICECCME55909.2022.9988338.
[2]. I. Rodriguez-Ferrandez, L. Kosmidis, M. M. Trompouki, D. Steenari, F. J. Cazorla, Evaluating the Computational Capabilities of Embedded Multicore and GPU Platforms for On-Board Image Processing, in 2023 European Data Handling & Data Processing Conference (EDHPC), (2023) 1–7. https://doi.org/10.23919/EDHPC59100.2023.10395928.
[3]. J. Suder, K. Podbucki, T. Marciniak, Power Requirements Evaluation of Embedded Devices for Real-Time Video Line Detection, Energies, 16 (2023) 6677. https://doi.org/10.3390/en16186677.
[4]. M. Barnell, C. Raymond, S. Smiley, D. Isereau, D. Brown, Ultra Low-Power Deep Learning Applications at the Edge with Jetson Orin AGX Hardware, in 2022 IEEE High Performance Extreme Computing Conference (HPEC), (2022) 1–4. https://doi.org/10.1109/HPEC55821.2022.9926369.
[5]. R. Brunelli, Computational Aspects of Template Matching, in Template Matching Techniques in Computer Vision, Wiley, (2009) 201–219. https://doi.org/10.1002/9780470744055.ch10.
[6]. Y. ABE, T. Fukuda, M. Shikano, F. Arai, Y. Tanaka, Vision Based Navigation System for Autonomous Mobile Robot. Locomotive Experiments Based on Variable Template Matching, JSME International Journal Series C, 43 (2000) 408–414. https://doi.org/10.1299/jsmec.43.408.
[7]. J. F. Henriques, R. Caseiro, P. Martins, J. Batista, High-Speed Tracking with Kernelized Correlation Filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (2015) 583–596. https://doi.org/10.1109/TPAMI.2014.2345390.
[8]. J. López-Fandiño, D. B. Heras, F. Argüello, Using heterogeneous computing and edge computing to accelerate anomaly detection in remotely sensed multispectral images, The Journal of Supercomputing, (2024). https://doi.org/10.1007/s11227-024-05918-z.
[9]. D. O. Dantas, H. Danilo Passos Leal, D. O. B. Sousa, Fast 2D and 3D image processing with OpenCL, in 2015 IEEE International Conference on Image Processing (ICIP), (2015) 4858–4862. https://doi.org/10.1109/ICIP.2015.7351730.
[10]. R. S. Dehal, C. Munjal, A. A. Ansari, A. S. Kushwaha, GPU Computing Revolution: CUDA, in 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), (2018) 197–201. https://doi.org/10.1109/ICACCCN.2018.8748495.
[2]. I. Rodriguez-Ferrandez, L. Kosmidis, M. M. Trompouki, D. Steenari, F. J. Cazorla, Evaluating the Computational Capabilities of Embedded Multicore and GPU Platforms for On-Board Image Processing, in 2023 European Data Handling & Data Processing Conference (EDHPC), (2023) 1–7. https://doi.org/10.23919/EDHPC59100.2023.10395928.
[3]. J. Suder, K. Podbucki, T. Marciniak, Power Requirements Evaluation of Embedded Devices for Real-Time Video Line Detection, Energies, 16 (2023) 6677. https://doi.org/10.3390/en16186677.
[4]. M. Barnell, C. Raymond, S. Smiley, D. Isereau, D. Brown, Ultra Low-Power Deep Learning Applications at the Edge with Jetson Orin AGX Hardware, in 2022 IEEE High Performance Extreme Computing Conference (HPEC), (2022) 1–4. https://doi.org/10.1109/HPEC55821.2022.9926369.
[5]. R. Brunelli, Computational Aspects of Template Matching, in Template Matching Techniques in Computer Vision, Wiley, (2009) 201–219. https://doi.org/10.1002/9780470744055.ch10.
[6]. Y. ABE, T. Fukuda, M. Shikano, F. Arai, Y. Tanaka, Vision Based Navigation System for Autonomous Mobile Robot. Locomotive Experiments Based on Variable Template Matching, JSME International Journal Series C, 43 (2000) 408–414. https://doi.org/10.1299/jsmec.43.408.
[7]. J. F. Henriques, R. Caseiro, P. Martins, J. Batista, High-Speed Tracking with Kernelized Correlation Filters, IEEE Transactions on Pattern Analysis and Machine Intelligence, 37 (2015) 583–596. https://doi.org/10.1109/TPAMI.2014.2345390.
[8]. J. López-Fandiño, D. B. Heras, F. Argüello, Using heterogeneous computing and edge computing to accelerate anomaly detection in remotely sensed multispectral images, The Journal of Supercomputing, (2024). https://doi.org/10.1007/s11227-024-05918-z.
[9]. D. O. Dantas, H. Danilo Passos Leal, D. O. B. Sousa, Fast 2D and 3D image processing with OpenCL, in 2015 IEEE International Conference on Image Processing (ICIP), (2015) 4858–4862. https://doi.org/10.1109/ICIP.2015.7351730.
[10]. R. S. Dehal, C. Munjal, A. A. Ansari, A. S. Kushwaha, GPU Computing Revolution: CUDA, in 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), (2018) 197–201. https://doi.org/10.1109/ICACCCN.2018.8748495.
Tải xuống
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Nhận bài
15/05/2024
Nhận bài sửa
11/06/2024
Chấp nhận đăng
14/06/2024
Xuất bản
15/09/2024
Chuyên mục
Công trình khoa học
Kiểu trích dẫn
Xuan Tung, V. (1726333200). Building an image processing program for the vehicle fire control system. Tạp Chí Khoa Học Giao Thông Vận Tải, 75(7), 2070-2080. https://doi.org/10.47869/tcsj.75.7.5
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