Deployment of Object Detection Enhanced with Multi-label Multi-classification on Edge Device

Ogheneuriri Oderhohwo, Tolulope A. Odetola, Hawzhin Mohammed, Syed Rafay Hasan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

This paper presents a method that enables detecting multiple mutually exclusive features of each class using the same convolutional neural network (CNN). This method proposes the design of a Multi-Label Multi-Classification (MLMC) deep learning model on resource-constrained edge devices. The proposed technique is also extended to utilizing the same CNN for classifying heterogeneous datasets. The MLMC model is used in conjunction with object detection techniques that leverage on a cropping system. The combined model used in conjunction with Intel's Neural Compute Stick (NCS) achieves the detection of an object of interest at the rate of 10.51FPS out-performing conventional object detection speed on edge devices.

Original languageEnglish (US)
Title of host publication2020 IEEE 63rd International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages986-989
Number of pages4
ISBN (Electronic)9781538629161
DOIs
StatePublished - Aug 2020
Event63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020 - Springfield, United States
Duration: Aug 9 2020Aug 12 2020

Publication series

NameMidwest Symposium on Circuits and Systems
Volume2020-August
ISSN (Print)1548-3746

Conference

Conference63rd IEEE International Midwest Symposium on Circuits and Systems, MWSCAS 2020
Country/TerritoryUnited States
CitySpringfield
Period8/9/208/12/20

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Electrical and Electronic Engineering

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