Abstract

Edge computing has seen a dramatic rise in demand, driven by the necessity for real-time, low-latency applications across various domains from autonomous vehicles to surveillance systems. Among these, real-time object detection stands as a crucial technology. However, the inherent constraints of edge devices, including limited computational power, present significant challenges.
This thesis provides a comprehensive evaluation of several Convolutional Neural Networks based object detection models when deployed on resource-constrained edge devices, specifically Raspberry Pi and Google’s Coral TPU. The models examined include EfficientDet, YOLO, and variants of the MobileNet family combined
with SSD for object detection tasks.
We developed a novel benchmarking framework that allowed the evaluation of these models under different configurations, enabling an accurate assessment of their performance characteristics. The benchmarking framework and the metrics used for evaluation can provide a foundation for future work, focusing on the design and deployment of efficient real-time object detection models on edge devices.
The performance of these models was scrutinized based on an exhaustive set of metrics including processing speed (frames per second), model accuracy (F1 score), energy consumption, CPU utilization, memory footprint, and device temperature.
A novel benchmarking framework was developed to evaluate these models under diverse configurations, providing a precise assessment of their respective performance characteristics. This benchmarking framework, along with the evaluation metrics, sets the foundation for future research concentrating on the design and deployment of efficient real-time object detection models on edge devices. The findings of this study underscore the fact that no single model is a universal solution for all edge applications; instead, the choice of model is heavily dependent on the specific requirements and constraints of the given application. By offering a detailed overview of the performance traits of each model, we aim to guide practitioners in making informed decisions when deploying object detection models in edge computing environments. This work sets the stage for future exploration in the development of more efficient and effective models for real-time object detection on edge devices