Sending pictures over radio systems of trail cam in border security and directing uavs to the right areas

Year 2024, Volume: 66 Issue: 2, 214 - 227

Vedat Yılmaz

https://doi.org/10.33769/aupse.1438139

Abstract

In this study, a method is proposed for the trail cams to send data via narrow band communication systems in border security and counter-terrorism areas and to direct drones to the right areas. The success of UAVs lies in scanning the correct areas for observation or detection. UAVs should be fed with data to observe the correct regions, and the probability of detecting border security or terrorist elements should be increased. Instantaneous detection is performed by trail cam, which generally operate dependent on GSM. However, these devices cannot provide real-time data in border areas with low population density and no GSM service, particularly in counter-terrorism operations. In this study, the dependence of trail cam devices on GSM was eliminated, and data transfer over the radio system was established to enable real-time data flow in a wide field. After the trail cam device makes a detection, the data is sent via the APCO-25 JEMUS radio system with a capacity of 9.6 KB. The resolution of the detection image is reduced, allowing it to be displayed on a remote-control computer in less than one minute. As a result of the study, when an intelligent trail cam with object recognition capability is developed, the device can assess what the image might be in real-time. Obtaining real time detection data from trail cams in border areas and counter-terrorism zones without GSM infrastructure can expedite the direction of UAVs to the correct regions for intervention by military units. Additionally, confirming that trail cam detects via narrowband communication systems in locations where units are temporarily stationed and without alpine terrain minimizes the surveillance vulnerability of UAVs unable to perform imaging due to adverse weather conditions. This also establishes a warning system against potential attacks by terrorist elements.

Keywords

Border security, trail cam, detection, reconnaissance, surveillance

References

• Lupp, G., Kantelberg, V., Förster, B., Honert, C., Naumann, J., Markmann, T., and Pauleit, S, Visitor counting and monitoring in forests using camera traps: A case study from Bavaria (Southern Germany), Land, 10 (7), (2021), 736, http://dx.doi.org/10.3390/land10070736.
• Şimşek, M., Yalçinkaya, F., Uğurlutan, R., Wide area scanning Trap Camera System with multi-cameras and distinctive motion detection sensor, 26th SIU IEEE, (2018), 1-4, https://doi.org/10.1109/SIU.2018.8404190.
• Albers, J. L., Wildhaber, M. L., Green, N. S., Struckhoff, M. A., and Hooper, M. J., Visitor use and activities detected using trail cameras at forest restoration sites, Ecological Restoration, 41 (4) (2023), 199-212, https://doi.org/10.3368/er.41.4.199.
• Norouzzadeh, M. S., Nguyen, A., Kosmala, M., Swanson, A., Palmer, M. S., Packer, C., and Clune, J., Automatically identifying, counting, and describing wild animals in camera-trap images with deep learning, Proc. Natl. Acad. Sci., 115 (25) (2018), E5716-E5725, https://doi.org/10.1073/pnas.1719367115.
• Schneider, S., Taylor, G. W., & Kremer, S. (2018, May). Deep learning object detection methods for ecological camera trap data, 15th CRV IEEE, (2018), 321-328, http://dx.doi.org/10.1109/CRV.2018.00052.
• Şimşek, E., Özyer, B., Bayındır, L., Özyer, G. T., Human-animal recognition in camera trap images, 26th SIU IEEE, (2018), 1-4, http://dx.doi.org/10.1109/SIU.2018.8404700.
• Roboteye, (2023). Available at: https://roboteye.ai/kapan-solo/. [Accessed January 2024].
• Rpboteye, (2023). Available at: https://www.linkedin.com/posts/roboteyeai_join-us-at the-world-defense-show-in-riyadh-activity-7158686794442539010-5Ko0. [Accessed January 2024].
• Yousif, H., Yuan, J., Kays, R., He, Z., Fast human-animal detection from highly cluttered camera-trap images using joint background modeling and deep learning classification, IEEE ISCAS, (2017), 1-4, http://dx.doi.org/10.1109/ISCAS.2017.8050762.
• Şimşek, E., Özyer, B., Özyer, G. T., Foto-kapan görüntülerinde derin öğrenme tabanlı insan tespiti, BÜ Fen Bil. Derg., 3 (1) (2020), 1-8.
• Şimşek, E., Bariş, Ö., Özyer, G. T., Foto-kapan görüntülerinde hareketli nesne tespiti ve konumunun belirlenmesi, Erzincan University J. Sci and Tech., 12 (2) (2019), 902-919, https://doi.org/10.18185/erzifbed.509571.
• ETSI, (2009). Additional spectrum requirements for future public safety and security wireless communication systems in the UHF range. System Reference Document; Land Mobile Service l. Available at: https://cept.org/files/9421/tr_102628v010101p.doc. [Accessed April 2023].
• Geylani, M., Çibuk, M., Çinar, H., ve Ağgün, F., Geçmişten günümüze hücresel haberleşme teknolojilerinin gelişimi, DEÜ Müh. Fak. Fen ve Müh. Derg., 18 (54) (2016), 606-623, http://dx.doi.org/10.21205/deufmd.2016185425.
• Qaddus, A., Real time performance analysis of Digital Mobile Radio (DMR) and APCO project 25 (P-25) radio systems in Land Mobile Radio (LMR) systems, Int. J. Comput. Eng. Inf. Tech., 8 (3) (2016), 49.
• Savunma Sanayi Başkanlığı (SSB), (2016). Sektör raporu. Available at: https://thinktech.stm.com.tr/uploads/docs/1608890536_stm-sektor-raporu-kamu guvenligi-ve-acil-yardim.pdf. [Accessed March 2023].
• Şahin, A. (2023). Depremde herşey sustu Aselsan JEMUS konuştu. Available at: https://www.savunmasanayist.com/depremde-her-sey-sustu-aselsan-jemus-konustu/. [Accessed September 2023].
• Şahin, F., Telsiz haberleşme standartlari, İstanbul Aydın Üniversitesi Dergisi, 27 (2015), (15-30), http://dx.doi.org/10.17932/IAU.IAUD.m.13091352.2015.7/27.15-30.
• Babel, L., Coordinated target assignment and UAV path planning with timing constraints, Intell. Robot. Syst., 94 (3-4) (2019), 857-869, http://dx.doi.org/10.1007/s10846-018-0910-9.
• Koslowski, R., Schulzke, M., Drones along borders: Border security UAVs in the United States and the European Union, Int. Stud. Perspect., 19 (4) (2018), 305-324, http://dx.doi.org/10.1093/isp/eky002.
• Haddal, C. C., Gertler, J., Homeland security: Unmanned aerial vehicles and border surveillance, (2010).
• Yildiz, B., Exploration of the use of unmanned aerial vehicles along with other assets to enhance border protection (Doctoral dissertation, Monterey, California. Naval Postgraduate School) (2009).
• Csernatoni, R., Constructing the EU’s high-tech borders: FRONTEX and dual-use drones for border management, European Security, 27 (2) (2018), 175-200, http://dx.doi.org/10.1080/09662839.2018.1481396.
• Villi, O., Yakar, M., İnsansız hava araçlarının kullanım alanları ve sensör tipleri, TİHA Dergisi, 4 (2) (2022), 73-100, http://dx.doi.org/10.51534/tiha.1189263.
• Arya, L., Rastogi, R., Study on aerial monitoring system in agriculture, forestry, defense, and border protection using artificial intelligence (AI), Agric. Aquacult. Appl. Biosens. Bioelectron, (2024), 389-404, http://dx.doi.org/10.4018/979-8-3693-2069-3.ch021.
• Şahiner, M. K., Ayhan, E. and Önder, M., Yeni sınır güvenliği anlayışında yapay zekâ yönetişimi: Fırsatlar ve tehditler, Ulisa, 5 (2) (2021), 83-95.
• Bakır, G., Insansiz hava araçlarinin savunma sanayi harcamasinda yeri ve önemi, ASEAD, 6 (2) (2019), 127-134, http://dx.doi.org/10.51534/tiha.884468.
• Newell, B. C., Gomez, R., Guajardo, V., Sensors, cameras, and the new'normal'in clandestine migration: How undocumented migrants experience surveillance at the US Mexico border, Surveillance and Society, 15 (1) (2017), 21-41, http://dx.doi.org/10.24908/ss.v15i1.5604.
• Berrahal, S., Kim, J. H., Rekhis, S., Boudriga, N., Wilkins, D., Acevedo, J., Border surveillance monitoring using quadcopter UAV-aided wireless sensor networks, J. Commun. Softw. Syst., 12 (1) (2016), 67-82, http://dx.doi.org/10.24138/jcomss.v12i1.92.
• Bahaghighat, M., Motamedi, S. A., Xin, Q., Image transmission over cognitive radio networks for smart grid applications, Applied Sciences, 9 (24) (2019), 5498, http://dx.doi.org/10.1109/MWC.2013.6590059.
• Grois, D., Marpe, D., Mulayoff, A., Itzhaky, B., Hadar, O., Performance comparison of h. 265/mpeg-hevc, vp9, and h. 264/mpeg-avc encoders, Picture Coding Symposium (PCS) IEEE, (2013), 394-397.
• Xiong, W., Lv, Y., Zhang, X., Cui, Y., Learning to translate for cross-source remote sensing image retrieval, IEEE Trans. Geosci. Remote Sens., 58 (7) (2020), 4860-4874, http://dx.doi.org/10.1109/TGRS.2020.2968096.
• Glass, S., Muthukkumarasamy, V., Portmann, M., A software-defined radio receiver for APCO Project 25 signals, Proceedings of the 2009 ICWCMC, (2009), 67-72, http://dx.doi.org/10.1145/1582379.1582395.
• Ramsey, E. R., A software based APCO Project 25 data transmission base station for local police headquarters, University of New Hampshire, (2007), http://dx.doi.org/10.1109/THS.2008.4534487.
• Khayam, S. A., The discrete cosine transform (DCT): theory and application, Michigan State University, 114 (1) (2003), 31.
• Ahmed, N., Natarajan, T., Rao, K. R., Discrete cosine transform, IEEE trans. on Comp., 100 (1) (1974), 90-93.
• Scribano, C., Franchini, G., Prato, M., Bertogna, M., DCT-Former: Efficient self attention with discrete cosine transform, J. Sci. Comput., 94 (3) (2023), 67, http://dx.doi.org/10.1007/s10915-023-02125-5.