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Optimized queue for real-time high-resolution (1920x1080) object detection and tracking with integrated GPS on resource-constrained edge devices

adaptive-and-self-organizing-systems
artificial-intelligence
computer-vision
real-time-and-embedded-systems
spatial-and-geographic-information-systems

Abstract

This study presents a YOLOv8-based system designed for real-time detection and tracking of critical objects such as road surface defects on a vehicle-mounted platform. The originality of this work lies in the redesign of the queue mechanism and parallel execution strategy, leading to the development of the Optimized Queue (OQ) approach.The proposed method was tested with three execution schemes: process-only, thread-only, and hybrid process–thread configurations, and compared against the Standard Queue (SQ). Experiments were carried out on an NVIDIA Jetson Orin Nano using a single shared video recorded with a GoPro HERO12 Black, ensuring a fair and consistent evaluation protocol.Results indicate that the OQ strategy substantially improves system efficiency. The number of dropped frames was reduced by about 45–47\%, average queue occupancy decreased from 100\% to 74\%, and throughput (FPS) increased by 15–19\%. Furthermore, in a field evaluation of 240 annotated objects, the detection rate under the hybrid execution scheme rose from 73.8\% to 94.2\%, while the number of missed objects declined from 63 to 14.These findings demonstrate that careful queue design not only accelerates processing but also maintains temporal continuity, thereby lowering the risk of missing safety-critical instances such as damaged road surfaces or open manholes. In this respect, the proposed OQ-based pipeline provides a practical contribution toward building low-latency and reliable object detection–tracking systems on resource-constrained edge devices.
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