Vehicle Speed Detection System Using IoT and Machine Learning
DOI:
https://doi.org/10.22105/siot.vi.60Keywords:
Vehicle speed detection, Internet of things, Machine learning, Smart city, Traffic managementAbstract
This paper outlines a detailed strategy for detecting vehicle speed, utilizing Internet of Things (IoTs) and Machine Learning (ML) technologies. Conventional radar-based systems for speed detection face challenges related to scalability, cost, and effectiveness in intricate settings. The suggested system combines IoTs sensors, including Light Detection and Ranging (LIDAR), radar, and high-definition cameras, with ML techniques such as You Only Look Once (YOLO) and regression models to achieve real-time speed detection and anomaly monitoring. The processing of data in real-time through edge and cloud computing allows for swift and effective traffic management solutions. Findings demonstrate enhanced accuracy, scalability, and cost efficiency, carrying significant consequences for the infrastructure of smart cities.
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