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OBJECT DETECTION AND ANALYSIS SYSTEM USING NEURAL NETWORKS AND INTEGRATION WITH LIDAR AND OMNIDIRECTIONAL CAMERAS

Trushin Stepan Mikhailovich  (Senior Lecturer, Department of Applied Mathematics, MIREA – Russian Technological University Russian Federation, Moscow )

Petrunev Egor Alekseevich  (MIREA – Russian Technological University, Russian Federation, Moscow)

Shmakov Kirill Alekseevich  (MIREA – Russian Technological University, Russian Federation, Moscow)

the article is devoted to the analysis of the object detection system based on neural networks with the integration of LiDAR and omnidirectional cameras. Particular attention is paid to the technological aspects, system architecture and its application in civil defense and the civilian sphere. The purpose of the study is to consider the benefits and challenges associated with the implementation of these technologies, as well as the prospects for their development. The work uses methods of analysis of modern research, system design and comparative evaluation of technologies. The results show that the combination of neural networks, LiDAR and 360-degree cameras significantly improves the accuracy and speed of object detection, which is relevant for security, transport and environmental monitoring. The findings highlight the need to address ethical and technical issues for further development of the technology.

Keywords:neural networks, lidar systems, panoramic cameras, object detection, sensor integration, computer vision, real-time data processing, autonomous systems, artificial intelligence, multimodal perception.
 

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Citation link:
Trushin S. M., Petrunev E. A., Shmakov K. A. OBJECT DETECTION AND ANALYSIS SYSTEM USING NEURAL NETWORKS AND INTEGRATION WITH LIDAR AND OMNIDIRECTIONAL CAMERAS // Современная наука: актуальные проблемы теории и практики. Серия: Естественные и Технические Науки. -2025. -№06/2. -С. 207-213 DOI 10.37882/2223-2966.2025.06-2.39
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