Unlock instant, AI-driven research and patent intelligence for your innovation.

Driver fatigue detection method and device based on convolutional neural network

A convolutional neural network and driver fatigue technology is applied in the field of driver fatigue detection to reduce the incidence of traffic accidents

Active Publication Date: 2019-08-13
SHANDONG INSPUR SCI RES INST CO LTD
View PDF5 Cites 8 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The technical task of the present invention is to provide a driver fatigue detection method and device based on convolutional neural network to solve how to use convolutional neural network to detect the fatigue degree of the driver, and to give an alarm to the driver who is already tired. The problem of reducing the incidence of traffic accidents

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Driver fatigue detection method and device based on convolutional neural network
  • Driver fatigue detection method and device based on convolutional neural network

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0044] as attached figure 1 Shown, the driver's fatigue detection method based on convolutional neural network of the present invention, this method comprises the steps:

[0045] S1. Collect face image data in various situations, and perform feature marking on the image; wherein, collect face image data in various situations including day and night and face images with eyes open and eyes closed in different environments ;The face image is taken by an infrared vehicle camera; among them, the face image can also be searched for suitable pictures on the Internet for marking, etc., and the pictures are not less than tens of thousands;

[0046] S2. Using the cnn convolutional neural network algorithm to train the image data set to obtain a corresponding model; the specific steps are as follows:

[0047] S201. Set two front convolution layers: including 32 convolution kernels, each convolution kernel is 3*3, the edge is not supplemented, the convolution step is 1 to the right and d...

Embodiment 2

[0064] as attached figure 2 As shown, the driver fatigue detection device based on the convolutional neural network, the device includes a host, a vehicle camera, a sound and a loading module, the loading module is installed in the host, and the host is respectively connected to and controls the vehicle camera and audio; the switch of the host is connected to the car or The locomotive starting device is connected to ensure that the host starts immediately when the car or locomotive is started; the camera is installed on the dashboard of the car or in the locomotive driver's cab where the facial expressions of the main driver can be captured without blocking the line of sight; the host and the audio are installed in the cab without The position that affects the driver's behavior or embedded in the center console; the loading module is used for camera shooting and processing.

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a driver fatigue detection method and device based on a convolutional neural network, belonging to the field of artificial intelligence, and aims to solve the technical problemof how to detect the fatigue degree of a driver by using the convolutional neural network and give an alarm to the fatigue driver, and the technical scheme comprises the following steps: S1, collecting face image data under various conditions, and carrying out feature marking on images; S2, training the image data set by adopting a cnn convolutional neural network algorithm to obtain a corresponding model; sS, compiling a corresponding python code loading program for the model trained in the step S2; S4, installing the loading program in the step S3 into a host of a center console in a cab; and S5, enabling the vehicle-mounted camera in the cab to shoot pictures of the driver and transmit the pictures to the loading program for reasoning, and judging whether the driver is in fatigue driving or not through the trained model. The invention further discloses a driver fatigue detection device based on the convolutional neural network.

Description

technical field [0001] The invention relates to the field of artificial intelligence, in particular to a driver fatigue detection method and device based on a convolutional neural network. Background technique [0002] Convolutional neural network is an efficient recognition method that has been developed in recent years and has attracted widespread attention. In the 1960s, Hubel and Wiesel found that its unique network structure can effectively reduce the complexity of the feedback neural network when studying the neurons used for local sensitivity and direction selection in the cat cerebral cortex, and then proposed the convolutional neural network ( Convolutional Neural Networks-referred to as CNN). Now, CNN has become one of the research hotspots in many scientific fields, especially in the field of pattern classification, because the network avoids the complex preprocessing of images and can directly input original images, so it has been more widely used. The new reco...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06V20/597G06N3/045G06F18/214
Inventor 于治楼戴鸿君裘肖明
Owner SHANDONG INSPUR SCI RES INST CO LTD