arXiv subjects

Daghash K. Alqahtani

Publications and source records attributed to Daghash K. Alqahtani.

3 recordsLinked to original sources

arXiv2026

A Comprehensive Evaluation of Deep Learning Object Detection Models on Heterogeneous Edge Devices

Modern applications such as autonomous vehicles, intelligent surveillance, and smart city systems increasingly require object detection on resource-constrained edge devices. Yet, there is still limited understanding of how different object detection models behave across heterogeneous edge devices and under varying scene complexity. In this paper, we benchmark YOLOv8 (Nano, Small, Medium), EfficientDet Lite (Lite0, Lite1, Lite2), and SSD (SSD MobileNet V1, SSDLite MobileDet) on Raspberry Pi 3, 4, 5 with/without Coral TPU accelerators, Raspberry Pi 5 with AI HAT+, Jetson Nano, and Jetson Orin Nano. We evaluate energy consumption, inference time, and accuracy, and further examine how accuracy changes with the number of objects in the input image. The results reveal clear trade-offs among accuracy, latency, and energy efficiency across model-device combinations. SSD MobileNet V1 achieves the lowest latency and energy consumption but the lowest accuracy, whereas YOLOv8 Medium achieves the highest accuracy at higher computational cost. TPU-based Raspberry Pi devices improve the efficiency of SSD and EfficientDet Lite while reducing YOLOv8 accuracy. Orin Nano offers the most favorable overall balance across most model families. The object-count-based analysis further shows that models achieve more similar accuracy on simpler images, while the accuracy gap widens as scene complexity increases.

cs.CV↗

arXiv2026

Multi-Objective Load Balancing for Heterogeneous Edge-Based Object Detection Systems

The rapid proliferation of the Internet of Things (IoT) and smart applications has led to a surge in data generated by distributed sensing devices. Edge computing is a mainstream approach to managing this data by pushing computation closer to the data source, typically onto resource-constrained devices such as single-board computers (SBCs). In such environments, the unavoidable heterogeneity of hardware and software makes effective load balancing particularly challenging. In this paper, we propose a multi-objective load balancing method tailored to heterogeneous, edge-based object detection systems. We study a setting in which multiple device-model pairs expose distinct accuracy, latency, and energy profiles, while both request intensity and scene complexity fluctuate over time. To handle this dynamically varying environment, our approach uses a two-stage decision mechanism: it first performs accuracy-aware filtering to identify suitable device-model candidates that provide accuracy within the acceptable range, and then applies a weighted-sum scoring function over expected latency and energy consumption to select the final execution target. We evaluate the proposed load balancer through extensive experiments on real-world datasets, comparing against widely used baseline strategies. The results indicate that the proposed multi-objective load balancing method halves energy consumption and achieves an 80% reduction in end-to-end latency, while incurring only a modest, up to 10%, decrease in detection accuracy relative to an accuracy-centric baseline.

cs.DC↗

arXiv2026

ECORE: Energy-Conscious Optimized Routing for Deep Learning Models at the Edge

Edge computing enables data processing closer to the source, significantly reducing latency, an essential requirement for real-time vision-based analytics such as object detection in surveillance and smart city environments. However, these tasks place substantial demands on resource-constrained edge devices, making the joint optimization of energy consumption and detection accuracy critical. To address this challenge, we propose ECORE, a framework that integrates multiple dynamic routing strategies, including a novel estimation-based techniques and an innovative greedy selection algorithm, to direct image processing requests to the most suitable edge device-model pair. ECORE dynamically balances energy efficiency and detection performance based on object characteristics. We evaluate our framework through extensive experiments on real-world datasets, comparing against widely used baseline techniques. The evaluation leverages established object detection models (YOLO, SSD, EfficientDet) and diverse edge platforms, including Jetson Orin Nano, Raspberry Pi 4 and 5, and TPU accelerators. Results demonstrate that our proposed context-aware routing strategies can reduce energy consumption and latency by 35% and 49%, respectively, while incurring only a 2% loss in detection accuracy compared to accuracy-centric methods.

cs.DC↗