YOLO-based Traffic Monitoring on Avenida Arequipa using IoT and Geospatial Data
Main Article Content
Abstract
This paper presents the design and evaluation of an intelligent traffic monitoring system
deployed on Avenida Arequipa, one of the main urban corridors in Lima, Peru. The system combines
Internet of Things (IoT) infrastructure, georeferenced video capture, and a YOLO-based deep learning
model to detect and classify road users in real time. Our approach focuses on low-cost cameras integrated
with embedded devices that perform local preprocessing and send metadata to a central server for storage,
analytics, and visualization. The geospatial configuration of Avenida Arequipa—its lane structure,
intersections, and bus-only segments—is explicitly modeled to support the estimation of vehicle density,
flow, and occupancy at different points of the avenue. A dataset of annotated images was built from
video streams recorded at multiple time periods, considering diverse lighting and traffic conditions. The
YOLO detector was trained to identify cars, buses, motorcycles, bicycles, pedestrians, and traffic lights.
Experimental results show that the proposed system achieves accurate detection performance, with a
mean Average Precision (mAP) above 0.90 for the most frequent classes, while maintaining an inference
time compatible with near real-time monitoring on commodity hardware. The study demonstrates
that combining YOLO with IoT and geospatial mapping in a real urban corridor is a feasible strategy to
support future smart city applications such as adaptive traffic control, public transport prioritization, and
safety incident analysis.
Article Details
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