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Title	A Modular Detection System For Smart Cities: Integrating Monocular And Lidar Solutions For Scalable Traffic Monitoring
ID_Doc	2740
Authors	Borau-Bernad J.; Ramajo-Ballester Á.; María Armingol Moreno J.; Sanchis de Miguel A.
Year	2025
Published	International Conference on Vehicle Technology and Intelligent Transport Systems, VEHITS - Proceedings
DOI	http://dx.doi.org/10.5220/0013195200003941
Abstract	As smart cities continue to develop, they require scalable and efficient traffic monitoring systems. This paper presents a modular detection system that switches between monocular and multimodal modes, depending on the available sensors. The monocular mode, based on the MonoLSS algorithm, offers a cost-effective vehicle detection solution using a single camera, ideal for simpler or low-budget setups. In contrast, the multimodal mode integrates camera and LiDAR data via the MVX-Net model, enhancing 3D accuracy in complex traffic scenarios. This dual-mode flexibility allows smart cities to adapt the system to their infrastructure and budgetary needs, ensuring scalability as urban demands evolve. Inference results demonstrate the superior accuracy of the multimodal approach in challenging environments while validating the efficiency of the monocular mode for simpler settings. Therefore, the modular detection system offers a flexible solution that optimizes both cost and performance, effectively addressing the varied requirements of smart city traffic management. Copyright © 2025 by SCITEPRESS - Science and Technology Publications, Lda.
Author Keywords	3D Object Detection; Autonomous Driving; Deep Learning; Intelligent Infrastructures; Smart Cities

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