Intelligent transportation
systems; Edge computing; YOLO; Domain adaptation; Weigh-in-motion; Smart cities
447
Thermal Vehicle Detection and Tracking for Intelligent
Transportation Systems: A Modular IoT Architecture and
Staged Deployment Roadmap
Mostafa Borhani1,*
1Smart Tech Services SPC, Muscat, Oman
Email: borhani@iSmartGCC.com
Received: March 01, 2025 Revised: June 20, 2025 Accepted: August 14, 2025
1. Introduction
Intelligent transportation systems (ITS) embedded in smart-city ecosystems need automated vehicle characterization—
detection, classification, and tracking—that holds up across the full range of real-world operating conditions [1]. The
primary sensor modalities in current practice each carry systemic weaknesses. Fixed weighing stations introduce
throughput bottlenecks and delay commercial traffic; in-pavement sensors require expensive excavation, degrade
under cyclic loading, and need periodic recalibration [1]. Visible-spectrum cameras—the most widely deployed
alternative—suffer performance degradation estimated at 30–50% under low illumination, shadows, glare, or adverse
weather [2]. These limitations create a genuine operational gap in environments where 24-hour reliability is
non-negotiable.
Journal of Intelligent Systems and Internet of Things
Vol. 18, No. 02, PP. 447-464, 2026
DOI: https://doi.org/10.54216/JISIoT.180231
Automated vehicle monitoring in intelligent transportation systems must operate reliably around the clock, including
under conditions that routinely cripple conventional visible-light cameras: night, glare, shadows, and adverse
weather. This paper proposes a modular Internet of Things (IoT) architecture for thermal-based vehicle detection,
classification, and trajectory analysis, together with a four-phase deployment roadmap that connects public-dataset
evaluation to live-traffic field validation. The system integrates longwave infrared (LWIR) imaging (8–14 𝜇m) with
YOLO-family deep learning detectors (YOLOv8/v11/v12) and multi-object tracking algorithms (ByteTrack, BoTSORT,
StrongSORT), deployed across NVIDIA Jetson edge devices and cloud infrastructure through JSON/MQTTformalized
data contracts. The primary novel contribution is a system-level integration framework that bridges the
gap between component-level algorithmic research and operational deployment. Concretely, this work: (i) defines
five functionally independent modules with explicit interface specifications and latency budgets not previously
formalized in the thermal-ITS literature; (ii) introduces quantified decision gates linking progression criteria directly
to published benchmark values; (iii) provides region-specific operational availability estimates derived from
empirical weather-degradation data; and (iv) integrates domain adaptation, GDPR compliance, edge hardware
budgets, and regulatory WIM frameworks within a single coherent system blueprint. Domain adaptation strategies
reported in peer-reviewed literature recover 20–50% of cross-dataset mAP degradation (typically 10–30%) caused
by sensor and scene variability; these figures are literature benchmarks, not results obtained in this work. An optional
weight-estimation module (Module 4) based on recent vision-based and bridge WIM validation studies is treated as
an exploratory extension requiring site-specific validation.