Technological prototype with artificial intelligence for military security in river environments
Maritime and river security is one of the main concerns of military forces due to the large number of illicit activities that occur. Not to mention the extensive areas that must be monitored, and the weather conditions that can occur. Currently, technologies have become fundamental to leave aside ma...
- Autores:
-
Sánchez, Sergio
Casillo, Daniel
Merlano, Andres
Barrera, Julian
Morales, Alex
Contreras, Elbert
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2024
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/13541
- Acceso en línea:
- https://doi.org/10.32397/tesea.vol5.n2.607
- Palabra clave:
- Artificial intelligence
Militar security
Raspberri py
Prototype
- Rights
- openAccess
- License
- https://creativecommons.org/licenses/by/4.0
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| dc.title.spa.fl_str_mv |
Technological prototype with artificial intelligence for military security in river environments |
| dc.title.translated.spa.fl_str_mv |
Technological prototype with artificial intelligence for military security in river environments |
| title |
Technological prototype with artificial intelligence for military security in river environments |
| spellingShingle |
Technological prototype with artificial intelligence for military security in river environments Artificial intelligence Militar security Raspberri py Prototype |
| title_short |
Technological prototype with artificial intelligence for military security in river environments |
| title_full |
Technological prototype with artificial intelligence for military security in river environments |
| title_fullStr |
Technological prototype with artificial intelligence for military security in river environments |
| title_full_unstemmed |
Technological prototype with artificial intelligence for military security in river environments |
| title_sort |
Technological prototype with artificial intelligence for military security in river environments |
| dc.creator.fl_str_mv |
Sánchez, Sergio Casillo, Daniel Merlano, Andres Barrera, Julian Morales, Alex Contreras, Elbert |
| dc.contributor.author.eng.fl_str_mv |
Sánchez, Sergio Casillo, Daniel Merlano, Andres Barrera, Julian Morales, Alex Contreras, Elbert |
| dc.subject.eng.fl_str_mv |
Artificial intelligence Militar security Raspberri py Prototype |
| topic |
Artificial intelligence Militar security Raspberri py Prototype |
| description |
Maritime and river security is one of the main concerns of military forces due to the large number of illicit activities that occur. Not to mention the extensive areas that must be monitored, and the weather conditions that can occur. Currently, technologies have become fundamental to leave aside manual surveillance for intelligent systems that allow remote sensing, traffic control, and object detection. Based on the aforementioned problems, the purpose of this research was to design a technological prototype with artificial vision based on an artificial intelligence model to detect water vessels and people in river environments as a support tool for military security. The prototype used at hardware level a Raspberry Pi 3 card and four pre-trained models based on R-CNN, YOLO, EfficientDet and SSD (single shot multibox). The best-performing model was the Mobilnet V2 SSD, with an mAP of 0.83 and an FPS of 5. Finally, this tool can contribute to strengthening the strategic, tactical, and operational capabilities of actors in the military intelligence sector, aimed at protecting sovereignty and territorial integrity to establish an environment of security in society. |
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2024 |
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2024-12-24 00:00:00 |
| dc.date.available.none.fl_str_mv |
2024-12-24 00:00:00 |
| dc.date.issued.none.fl_str_mv |
2024-12-24 |
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Artículo de revista |
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Journal article |
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https://doi.org/10.32397/tesea.vol5.n2.607 |
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10.32397/tesea.vol5.n2.607 |
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2745-0120 |
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https://doi.org/10.32397/tesea.vol5.n2.607 |
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eng |
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eng |
| dc.relation.references.eng.fl_str_mv |
Luis Orozco, Mikel Ibarra, Alejandro Chaparro Ortiz, Adriana Castañeda, Armando Triana, Carlos Forero, Carlos Moreno, Diego Peñuela, and Paula Ortíz. Tecnologías emergentes para la seguridad y defensa nacional: los retos de los sistemas ciberfísicos para luchar contra el crimen organizado transnacional. 06 2021. [2] Mrunalini Nalamati, Nabin Sharma, Muhammad Saqib, and Michael Blumenstein. Automated monitoring in maritime video surveillance system. In 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ), pages 1–6, 2020. [3] Kwang-Il Kim, Jung Jeong, and Byung-Gil Lee. Study on the analysis of near-miss ship collisions using logistic regression. Journal of Advanced Computational Intelligence and Intelligent Informatics, 21:467–473, 05 2017. [4] Kwang-Il Kim, Jung Jeong, and Byung-Gil Lee. Study on the analysis of near-miss ship collisions using logistic regression. Journal of Advanced Computational Intelligence and Intelligent Informatics, 21:467–473, 05 2017. [5] Daewon Park. Syntactic-level integration and display of multiple domains’ s-100-based data for e-navigation. Cluster Computing, 20, 03 2017. [6] Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He. A comprehensive survey on transfer learning, 2020. [7] Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He. A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1):43–76, 2021. [8] Zhuangdi Zhu, Kaixiang Lin, and Jiayu Zhou. Transfer learning in deep reinforcement learning: A survey, 09 2020. [9] Puja Bharati and Ankita Pramanik. Deep Learning Techniques—R-CNN to Mask R-CNN: A Survey, pages 657–668. 01 2020. [10] Ross Girshick. Fast r-cnn. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 1440–1448, 2015. [11] Madhusri Maity, Sriparna Banerjee, and Sheli Chaudhuri. Faster r-cnn and yolo based vehicle detection: A survey. 04 2021. [12] Peiyuan Jiang, Daji Ergu, Fangyao Liu, Ying Cai, and Bo Ma. A review of yolo algorithm developments. Procedia Computer Science, 199:1066–1073, 2022. The 8th International Conference on Information Technology and Quantitative Management (ITQM 2020 2021): Developing Global Digital Economy after COVID-19. [13] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. SSD: Single Shot MultiBox Detector, page 21–37. Springer International Publishing, 2016. [14] Wen Liu, Weiqiao Yuan, Xinqiang Chen, and Yuxu Lu. An enhanced cnn-enabled learning method for promoting ship detection in maritime surveillance system. Ocean Engineering, 235:109435, 07 2021. [15] Mohana Mohana. Object detection and classification algorithms using deep learning for video surveillance applications. International Journal of Innovative Technology and Exploring Engineering, 8:386–395, 06 2019. [16] Yuxin Dong, Fukun Chen, Shuang Han, and Hao Liu. Ship object detection of remote sensing image based on visual attention. Remote Sensing, 13(16), 2021. [17] Yali Amit, Pedro Felzenszwalb, and Ross Girshick. Object Detection, pages 1–9. 01 2020. [18] Zhengxia Zou, Keyan Chen, Zhenwei Shi, Yuhong Guo, and Jieping Ye. Object detection in 20 years: A survey, 2023. [19] Xin Huang, Xinxin Wang, Wenyu Lv, Xiaying Bai, Xiang Long, Kaipeng Deng, Qingqing Dang, Shumin Han, Qiwen Liu, Xiaoguang Hu, Dianhai Yu, Yanjun Ma, and Osamu Yoshie. Pp-yolov2: A practical object detector, 2021. [20] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Region-based convolutional networks for accurate object detection and segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(1):142–158, 2016. [21] Xingxing Xie, Gong Cheng, Jiabao Wang, Xiwen Yao, and Junwei Han. Oriented r-cnn for object detection, 2021. [22] Wei Fang, Lin Wang, and Peiming Ren. Tinier-yolo: A real-time object detection method for constrained environments. IEEE Access, 8:1935–1944, 2020. [23] Chengji Liu, Yufan Tao, Jiawei Liang, Kai Li, and Yihang Chen. Object detection based on yolo network. pages 799–803, 12 2018. [24] Mingxing Tan, Ruoming Pang, and Quoc V. Le. Efficientdet: Scalable and efficient object detection. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10778–10787, 2020. [25] Delia Velasco-Montero, Jorge Fernández-Berni, Ricardo Carmona-Galan, and Ángel Rodríguez-Vázquez. Performance analysis of real-time dnn inference on raspberry pi. page 14, 05 2018. [26] Sheping Zhai, Dingrong Shang, Shuhuan Wang, and Susu Dong. Df-ssd: An improved ssd object detection algorithm based on densenet and feature fusion. IEEE Access, 8:24344–24357, 2020. [27] Wu Zheng, Weiliang Tang, Li Jiang, and Chi-Wing Fu. Se-ssd: Self-ensembling single-stage object detector from point cloud, 2021. |
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Transactions on Energy Systems and Engineering Applications |
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Núm. 2 , Año 2024 : Transactions on Energy Systems and Engineering Applications |
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Universidad Tecnológica de Bolívar |
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Repositorio Digital Universidad Tecnológica de Bolívar |
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Sánchez, SergioCasillo, DanielMerlano, AndresBarrera, JulianMorales, AlexContreras, Elbert2024-12-24 00:00:002024-12-24 00:00:002024-12-24Maritime and river security is one of the main concerns of military forces due to the large number of illicit activities that occur. Not to mention the extensive areas that must be monitored, and the weather conditions that can occur. Currently, technologies have become fundamental to leave aside manual surveillance for intelligent systems that allow remote sensing, traffic control, and object detection. Based on the aforementioned problems, the purpose of this research was to design a technological prototype with artificial vision based on an artificial intelligence model to detect water vessels and people in river environments as a support tool for military security. The prototype used at hardware level a Raspberry Pi 3 card and four pre-trained models based on R-CNN, YOLO, EfficientDet and SSD (single shot multibox). The best-performing model was the Mobilnet V2 SSD, with an mAP of 0.83 and an FPS of 5. Finally, this tool can contribute to strengthening the strategic, tactical, and operational capabilities of actors in the military intelligence sector, aimed at protecting sovereignty and territorial integrity to establish an environment of security in society.application/pdfengUniversidad Tecnológica de BolívarSergio Sánchez, Daniel Casillo, Andres Merlano, Julian Barrera, Alex Morales, Elbert Contreras - 2024https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessThis work is licensed under a Creative Commons Attribution 4.0 International License.http://purl.org/coar/access_right/c_abf2https://revistas.utb.edu.co/tesea/article/view/607Artificial intelligenceMilitar securityRaspberri pyPrototypeTechnological prototype with artificial intelligence for military security in river environmentsTechnological prototype with artificial intelligence for military security in river environmentsArtículo de revistainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Journal articleTextinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85https://doi.org/10.32397/tesea.vol5.n2.60710.32397/tesea.vol5.n2.6072745-0120Luis Orozco, Mikel Ibarra, Alejandro Chaparro Ortiz, Adriana Castañeda, Armando Triana, Carlos Forero, Carlos Moreno, Diego Peñuela, and Paula Ortíz. Tecnologías emergentes para la seguridad y defensa nacional: los retos de los sistemas ciberfísicos para luchar contra el crimen organizado transnacional. 06 2021. [2] Mrunalini Nalamati, Nabin Sharma, Muhammad Saqib, and Michael Blumenstein. Automated monitoring in maritime video surveillance system. In 2020 35th International Conference on Image and Vision Computing New Zealand (IVCNZ), pages 1–6, 2020. [3] Kwang-Il Kim, Jung Jeong, and Byung-Gil Lee. Study on the analysis of near-miss ship collisions using logistic regression. Journal of Advanced Computational Intelligence and Intelligent Informatics, 21:467–473, 05 2017. [4] Kwang-Il Kim, Jung Jeong, and Byung-Gil Lee. Study on the analysis of near-miss ship collisions using logistic regression. Journal of Advanced Computational Intelligence and Intelligent Informatics, 21:467–473, 05 2017. [5] Daewon Park. Syntactic-level integration and display of multiple domains’ s-100-based data for e-navigation. Cluster Computing, 20, 03 2017. [6] Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He. A comprehensive survey on transfer learning, 2020. [7] Fuzhen Zhuang, Zhiyuan Qi, Keyu Duan, Dongbo Xi, Yongchun Zhu, Hengshu Zhu, Hui Xiong, and Qing He. A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1):43–76, 2021. [8] Zhuangdi Zhu, Kaixiang Lin, and Jiayu Zhou. Transfer learning in deep reinforcement learning: A survey, 09 2020. [9] Puja Bharati and Ankita Pramanik. Deep Learning Techniques—R-CNN to Mask R-CNN: A Survey, pages 657–668. 01 2020. [10] Ross Girshick. Fast r-cnn. In 2015 IEEE International Conference on Computer Vision (ICCV), pages 1440–1448, 2015. [11] Madhusri Maity, Sriparna Banerjee, and Sheli Chaudhuri. Faster r-cnn and yolo based vehicle detection: A survey. 04 2021. [12] Peiyuan Jiang, Daji Ergu, Fangyao Liu, Ying Cai, and Bo Ma. A review of yolo algorithm developments. Procedia Computer Science, 199:1066–1073, 2022. The 8th International Conference on Information Technology and Quantitative Management (ITQM 2020 2021): Developing Global Digital Economy after COVID-19. [13] Wei Liu, Dragomir Anguelov, Dumitru Erhan, Christian Szegedy, Scott Reed, Cheng-Yang Fu, and Alexander C. Berg. SSD: Single Shot MultiBox Detector, page 21–37. Springer International Publishing, 2016. [14] Wen Liu, Weiqiao Yuan, Xinqiang Chen, and Yuxu Lu. An enhanced cnn-enabled learning method for promoting ship detection in maritime surveillance system. Ocean Engineering, 235:109435, 07 2021. [15] Mohana Mohana. Object detection and classification algorithms using deep learning for video surveillance applications. International Journal of Innovative Technology and Exploring Engineering, 8:386–395, 06 2019. [16] Yuxin Dong, Fukun Chen, Shuang Han, and Hao Liu. Ship object detection of remote sensing image based on visual attention. Remote Sensing, 13(16), 2021. [17] Yali Amit, Pedro Felzenszwalb, and Ross Girshick. Object Detection, pages 1–9. 01 2020. [18] Zhengxia Zou, Keyan Chen, Zhenwei Shi, Yuhong Guo, and Jieping Ye. Object detection in 20 years: A survey, 2023. [19] Xin Huang, Xinxin Wang, Wenyu Lv, Xiaying Bai, Xiang Long, Kaipeng Deng, Qingqing Dang, Shumin Han, Qiwen Liu, Xiaoguang Hu, Dianhai Yu, Yanjun Ma, and Osamu Yoshie. Pp-yolov2: A practical object detector, 2021. [20] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Region-based convolutional networks for accurate object detection and segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38(1):142–158, 2016. [21] Xingxing Xie, Gong Cheng, Jiabao Wang, Xiwen Yao, and Junwei Han. Oriented r-cnn for object detection, 2021. [22] Wei Fang, Lin Wang, and Peiming Ren. Tinier-yolo: A real-time object detection method for constrained environments. IEEE Access, 8:1935–1944, 2020. [23] Chengji Liu, Yufan Tao, Jiawei Liang, Kai Li, and Yihang Chen. Object detection based on yolo network. pages 799–803, 12 2018. [24] Mingxing Tan, Ruoming Pang, and Quoc V. Le. Efficientdet: Scalable and efficient object detection. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 10778–10787, 2020. [25] Delia Velasco-Montero, Jorge Fernández-Berni, Ricardo Carmona-Galan, and Ángel Rodríguez-Vázquez. Performance analysis of real-time dnn inference on raspberry pi. page 14, 05 2018. [26] Sheping Zhai, Dingrong Shang, Shuhuan Wang, and Susu Dong. Df-ssd: An improved ssd object detection algorithm based on densenet and feature fusion. IEEE Access, 8:24344–24357, 2020. [27] Wu Zheng, Weiliang Tang, Li Jiang, and Chi-Wing Fu. Se-ssd: Self-ensembling single-stage object detector from point cloud, 2021.Transactions on Energy Systems and Engineering Applications5111https://revistas.utb.edu.co/tesea/article/download/607/426Núm. 2 , Año 2024 : Transactions on Energy Systems and Engineering Applications220.500.12585/13541oai:repositorio.utb.edu.co:20.500.12585/135412025-09-16 09:15:14.218https://creativecommons.org/licenses/by/4.0Sergio Sánchez, Daniel Casillo, Andres Merlano, Julian Barrera, Alex Morales, Elbert Contreras - 2024metadata.onlyhttps://repositorio.utb.edu.coRepositorio Digital Universidad Tecnológica de Bolívarbdigital@metabiblioteca.com |