The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Sep. 10, 2024

Filed:

Apr. 28, 2022
Applicant:

Anhui University, Hefei, CN;

Inventors:

Jie Chen, Hefei, CN;

Bing Li, Hefei, CN;

Zihan Cheng, Hefei, CN;

Haitao Wang, Hefei, CN;

Jingmin Xi, Hefei, CN;

Yingjian Deng, Hefei, CN;

Assignee:

Anhui University, Hefei, CN;

Attorney:
Primary Examiner:
Int. Cl.
CPC ...
G06V 10/82 (2022.01); G06V 20/59 (2022.01); G06V 20/70 (2022.01);
U.S. Cl.
CPC ...
G06V 10/82 (2022.01); G06V 20/597 (2022.01); G06V 20/70 (2022.01);
Abstract

The present disclosure provides a method for fine-grained detection of driver distraction based on unsupervised learning, belonging to the field of driving behavior analysis. The method includes: acquiring distracted driving image data; and inputting the acquired distracted driving image data into an unsupervised learning detection model, analyzing the distracted driving image data by using the unsupervised learning detection model, and determining a driver distraction state according to an analysis result. The unsupervised learning detection model includes a backbone network, projection heads, and a loss function; the backbone network is a RepMLP network structure incorporating a multilayer perceptron (MLP); the projection heads are each an MLP incorporating a residual structure; and the loss function is a loss function based on contrastive learning and a stop-gradient strategy.


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