Hybrid Convolutional-Transformer Neural Network for Driver Distraction Detection-Reference-Cited by-同舟云学术

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Hybrid Convolutional-Transformer Neural Network for Driver Distraction Detection

Published:2023-09-22 Issue: Volume: Page:
ISSN:
Container-title:2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS)
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Author:

Li Penghua¹,Mou Qiyun¹,Hou Jie²,Tu Yushan³

Affiliation:

1. Chongqing University of Posts and Telecommunications,Key Laboratory of Intelligent Computing for Big Data, College of Automation,Chongqing,China

2. Chongqing University of Posts and Telecommunications,College of Automation,Chongqing,China

3. Chongqing University of Posts and Telecommunications,College of Computer Science and Technology,Chongqing,China

Funder

National Natural Science Foundation of China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10295546/10295577/10295832.pdf?arnumber=10295832

Reference18 articles.

1. Driver Distraction Detection Using Octave-Like Convolutional Neural Network

2. L2 regularization for learning kernels[J];cortes,2012

3. Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

4. Real-time distracted driver posture classification;abouelnaga,2017

5. Training data-efficient image transformers distillation through attention[C];touvron;International Conference on Machine Learning,2021

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