Research on the design technology of mobile Mix-YOLOv5s model

Luxuan Yu; Bicheng Zhang; Jingzhuo Zhang

doi:10.1117/12.2667786

22 February 2023 Research on the design technology of mobile Mix-YOLOv5s model

Luxuan Yu, Bicheng Zhang, Jingzhuo Zhang

Author Affiliations +

Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 1258703 (2023) https://doi.org/10.1117/12.2667786
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China

Abstract

Because the memory resources and computing resources of embedded devices are very limited, it is very difficult to deploy deep learning models on embedded devices. Therefore, how to efficiently and conveniently extract more feature maps from the limited feature maps in convolutional neural network has become a mainstream research direction. In the era of epidemic normalization, in order to deploy a mask face detection system more suitable for the actual scene in densely populated places such as communities, shopping malls, airports, railway stations and so on. This paper proposes the Mix-YOLOv5s model, which mainly combines the YOLOv5s model with the lightweight Ghost module, the CBAM attention module, Bidirectional Feature Pyramid Network(BiFPN), Mish activation function and Alpha-IoU loss function. They are used to promote the object detection ability of the model. We make quantitative and qualitative comparison between Mix-YOLOv5s and YOLOv5s. Compared with the YOLOv5s model, this model is able to improve the comprehensive performance by a small amount on the basis of greatly reducing Parameters and GFLOPs. Therefore, Mix-YOLOv5s model has great significance and advantages in the design of mobile terminal deep learning model, which can correctly judge whether the subjects wear masks, and has strong research value and broad application prospects.

Citation Download Citation

Luxuan Yu, Bicheng Zhang, and Jingzhuo Zhang "Research on the design technology of mobile Mix-YOLOv5s model", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 1258703 (22 February 2023); https://doi.org/10.1117/12.2667786

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
10 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Detection and tracking algorithms

Systems modeling

Design and modelling

Object detection

Neural networks

Deep learning

Instrument modeling

Show All Keywords

Keywords/Phrases

Search In:

Publication Years