Face detection with YOLO on edge.
Ali-Gombe, Adamu; Elyan, Eyad; Moreno-García, Carlos Francisco; Zwiegelaar, Johan
Professor Eyad Elyan email@example.com
Dr Carlos Moreno-Garcia firstname.lastname@example.org
Senior Lecturer (A)
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.
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|
|Series Title||Proceedings of the International Neural Networks Society (INNS)|
|Book Title||Proceedings of the 22nd Enginering applications of neural networks conference (EANN2021)|
|Keywords||Deep learning; YOLO; Face detection|
This file is under embargo until Jul 2, 2022 due to copyright reasons.
Contact email@example.com to request a copy for personal use.
You might also like
Multiple fake classes GAN for data augmentation in face image dataset.
Few-shot classifier GAN.
Deep learning for symbols detection and classification in engineering drawings.