CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13259 > Article
Paper
4 September 2024 Circuit design for driving distracted behavior recognition based on memristive neural network
Guofu Luo, Xuyao Zhang, Hao Li, Qichao Jiao, Gen Liu, Biao Deng, Haoqi Wang, Xinyu Yang
Author Affiliations +
Proceedings Volume 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024); 132593Q (2024) https://doi.org/10.1117/12.3039377
Event: Fourth International Conference on Automation Control, Algorithm, and Intelligent Bionics (ICAIB 2024), 2024, Yinchuan, China
Abstract
Driving distraction is one of the primary causes of frequent traffic accidents. Image data-based driving distraction recognition technology has advantages such as low cost, easy deployment, high efficiency, and accuracy, which is of significant importance for improving traffic safety levels. In this paper, the VGG16 network model is adopted, and the SF dataset is preprocessed, followed by training and testing the network. The experimental results demonstrate that VGG16 achieves an average accuracy of 97.82% in recognizing 10 driving behaviors. Additionally, a circuit design scheme for the various modules of VGG16 based on memristors is proposed, and simulations are performed on the MNSIM platform. Compared to the detection results running on a GPU, the accuracy loss is only around 5%.
© (2024) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Guofu Luo, Xuyao Zhang, Hao Li, Qichao Jiao, Gen Liu, Biao Deng, Haoqi Wang, and Xinyu Yang "Circuit design for driving distracted behavior recognition based on memristive neural network", Proc. SPIE 13259, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2024), 132593Q (4 September 2024); https://doi.org/10.1117/12.3039377
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
 6 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Neural networks
Design
Education and training
Simulations
Convolution
Convolutional neural networks
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top