Tehnički vjesnik, Vol. 33 No. 5, 2026.
Izvorni znanstveni članak
https://doi.org/10.17559/TV-20251127003167
Development of Multisensory UAV Technology for Automatic Foreign-Object Detection on Transport Infrastructure
Askar Abdykadyrov
; 1) Satbayev University, Satbayev str., 22, Almaty, Republic of Kazakhstan, 050013 2) Institute of Mechanics and Mechanical Engineering named after Academician U. A. Dzholdasbekov, Kurmangazy str., 29, Almaty, Republic of Kazakhstan, 050010 3) Tashkent Institute of Irrigation and Agricultural Mechanization Engineers, 39 Kori Niyoziy St., Mirzo Ulugbek District, Tashkent 100000, Uzbekistan
Nurzhigit Smailov
; Satbayev University, Satbayev str., 22, Almaty, Republic of Kazakhstan, 050013
Maxat Mamadiyarov
; Satbayev University, Satbayev str., 22, Almaty, Republic of Kazakhstan, 050013
Yessen Bagdollauly
; Satbayev University, Satbayev str., 22, Almaty, Republic of Kazakhstan, 050013
Abdurazak Kasimov
; Department of Telecommunications and Innovative Technologies, Gumarbek Daukeev Almaty University of Power Engineering and Communications, Baitursynuly str., 126/1, Almaty, Republic of Kazakhstan, 050013
Koptleu Bazhikov
; Department of Energy and Automation, Nanotechnology, Caspian University of Technology and Engineering named after Sh. Yessenov, Microdistrict 33, Aktau, Republic of Kazakhstan, 130000
Anar Khabay
; Satbayev University, Satbayev str., 22, Almaty, Republic of Kazakhstan, 050013
Nurlan Kystaubayev
; Satbayev University, Satbayev str., 22, Almaty, Republic of Kazakhstan, 050013
Yersaiyn Mailybayev
; International University of Transportation and Humanities, Almaty, Republic of Kazakhstan, 050013
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* Dopisni autor.
Sažetak
This study focuses on the development of a multisensory unmanned aerial vehicle (UAV) system for automatic detection of foreign objects on transport infrastructure. The research addresses the limitations of traditional monitoring methods, such as low detection accuracy and limited coverage, by integrating optical, thermal, and radar sensors with advanced AI models (YOLOv8x and RPN+STN). The developed system demonstrated a detection accuracy of 98% and a detection range of up to 350 m during laboratory tests, while field trials on a 5 km highway and 2 km railway section achieved an overall accuracy of 92.0% and a false positive rate of 4.1%. These results are explained by the synergistic combination of multisensory data fusion and real-time AI processing, enabling robust detection in diverse environmental conditions. The distinctive feature of the proposed system is its ability to operate with high precision and low latency (15 FPS, 120-250 ms) in real-time scenarios. The system is suitable for practical deployment in road and railway monitoring, particularly for improving transport safety in regions with limited human resources and challenging terrain.
Ključne riječi
data fusion; foreign object detection; multisensory systems; real-time monitoring; transport infrastructure; unmanned aerial vehicles (UAVs)
Hrčak ID:
350426
URI
Datum izdavanja:
31.8.2026.
Posjeta: 0 *