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Face detection with YOLO on edge.

Ali-Gombe, Adamu; Elyan, Eyad; Moreno-García, Carlos Francisco; Zwiegelaar, Johan

Authors

Adamu Ali-Gombe

Johan Zwiegelaar



Contributors

Lazaros Iliadis
Editor

John Macintyre
Editor

Chrisina Jayne
Editor

Elias Pimenidis
Editor

Abstract

Significant progress has been achieved in objects detection applications such as Face Detection. This mainly due to the latest development in deep learning-based approaches and especially in the computer vision domain. However, deploying deep-learning methods require huge computational power such as graphical processing units. These computational requirements make using such methods unsuitable for deployment on platforms with limited resources, such as edge devices. In this paper, we present an experimental framework to reduce the model’s size systematically, aiming at obtaining a small-size model suitable for deployment in a resource-limited environment. This was achieved by systematic layer removal and filter resizing. Extensive experiments were carried out using the “You Only Look Once” model (YOLO v3-tiny). For evaluation purposes, we used two public datasets to assess the impact of the model’s size reduction on a common computer vision task such as face detection. Results show clearly that, a significant reduction in the model’s size, has a very marginal impact on the overall model’s performance. These results open new directions towards further investigation and research to accelerate the use of deep learning models on edge-devices.

Citation

ALI-GOMBE, A., ELYAN, E., MORENO-GARCIA, C.F. and ZWIEGELAAR, J. 2021. Face detection with YOLO on edge. In Iliadis, L., Macintyre, J., Jayne, C. and Pimenidis, E. (eds.). Proceedings of the 22nd Enginering applications of neural networks conference (EANN2021), 25-27 June 2021, Halkidiki, Greece. Proceedings of the International Neural Networks Society (INNS), 3. Cham: Springer [online], pages 284-292. Available from: https://doi.org/10.1007/978-3-030-80568-5_24

Conference Name 22nd Enginering applications of neural networks conference (EANN2021)
Conference Location Halkidiki, Greece
Start Date Jun 25, 2021
End Date Jun 27, 2021
Acceptance Date Apr 7, 2021
Online Publication Date Jul 1, 2021
Publication Date Dec 31, 2021
Deposit Date Jun 25, 2021
Publicly Available Date Jul 2, 2022
Publisher Springer
Pages 284-292
Series Title Proceedings of the International Neural Networks Society (INNS)
Series Number 3
Series ISSN 2661-8141
Book Title Proceedings of the 22nd Enginering applications of neural networks conference (EANN2021)
ISBN 9783030805678
DOI https://doi.org/10.1007/978-3-030-80568-5_24
Keywords Deep learning; YOLO; Face detection
Public URL https://rgu-repository.worktribe.com/output/1369880

Files

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