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2020 | OriginalPaper | Buchkapitel

Using CNN and Channel Attention Mechanism to Identify Driver’s Distracted Behavior

verfasst von : Lu Ye, Cheng Chen, Mingwei Wu, Samuel Nwobodo, Annor Arnold Antwi, Chido Natasha Muponda, Koi David Ernest, Rugamba Sadam Vedaste

Erschienen in: Transactions on Edutainment XVI

Verlag: Springer Berlin Heidelberg

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Abstract

The driver’s distracted attention will cause a huge safety hazard to the traffic. In different types of distraction, it is illegal to make phone calls and smoke while driving, which will be fined in China. In order to solve this problem, a method of driver’s distracted behavior detection based on channel attention convolution neural network is proposed. SE module is added to the Xception network, which can distinguish the importance of different feature channels and enhance the expression ability of the network. SE module mainly assigns different weights to features to enhance more important features and suppress less influential features. The experiment uses Xception and SE-Xception for comparison. The experimental results show that the accuracy of SE-Xception is 92.60%, which has a good performance for the distracted driving behavior detection of drivers.

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Metadaten
Titel
Using CNN and Channel Attention Mechanism to Identify Driver’s Distracted Behavior
verfasst von
Lu Ye
Cheng Chen
Mingwei Wu
Samuel Nwobodo
Annor Arnold Antwi
Chido Natasha Muponda
Koi David Ernest
Rugamba Sadam Vedaste
Copyright-Jahr
2020
Verlag
Springer Berlin Heidelberg
DOI
https://doi.org/10.1007/978-3-662-61510-2_17