An Evaluation of Convolutional Neural Network Models for Object Detection in Images on Low-End Devices

Published in The 26th Irish Conference on Artificial Intelligence and Cognitive Science, 2018

Abstract

This research paper investigates the running of object detection algorithms on low-end devices to detect individuals in images while leveraging cloud-based services to provide facial verification of the individuals detected. The performance of three computer vision object detection algorithms that utilize Convolutional Neural Networks (CNN) are compared: SSD MobileNet, Inception v2 and Tiny YOLO along with three cloud-based facial verification services: Kairos, Amazon Web Service Rekognition (AWS) and Microsoft Azure Vision API. The results contribute to the limitations of running CNN based algorithms to solve vision tasks on low-end devices and highlights the limitations of using such devices and models in an application domain such as a home-security solution.

Paper ResearchGate

Recommended citation: David Foley, Ruairi O'Reilly (2018). “An Evaluation of Convolutional Neural Network Models for Object Detection in Images on Low-End Devices.” The 26th Irish Conference on Artificial Intelligence and Cognitive Science.
Download Paper