Driving fatigue degree detection regression model based on dual network result

A technology for network results and driving fatigue, applied in biological neural network models, character and pattern recognition, instruments, etc., can solve the problems of calculation result deviation, fatigue detection range limitation, etc., and achieve high feasibility, multiple detection parameters, and reference data wide range of effects

Active Publication Date: 2018-09-28
合肥湛达智能科技有限公司
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Problems solved by technology

[0004] The purpose of the present invention is to provide a driving fatigue degree detection regression model based on double network results to solve the problems of limited fatigue detection range and deviations in calculation results proposed in the above-mentioned background technology

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  • Driving fatigue degree detection regression model based on dual network result
  • Driving fatigue degree detection regression model based on dual network result
  • Driving fatigue degree detection regression model based on dual network result

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Embodiment Construction

[0046] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0047] Such as figure 1 As shown, a driving fatigue detection regression model based on the results of the dual network, the video shot every minute is input into the first convolutional network frame by frame, the driver's head is framed and whether the driver's head is lowered, and if the head is lowered, the statistics Add one to the number of middle and lower head samples; otherwise, perform image processing on the framed head position image, cut out the f...

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Abstract

The invention discloses a driving fatigue degree detection regression model based on a dual network result, comprising the following steps: S1, acquiring an existing data set of a human face in different angles or collecting videos shot by a camera in static and moving vehicles as sample video data, and carrying out size treatment and manually annotating; S2, inputting a picture set processed in the step S1 into a first convolutional network, and obtaining a head candidate window and a head state by the convolutional network; S3, processing a picture set with the heads not lowered in the headcandidate window in the step S2, then inputting into a second convolutional network, and obtaining states of human eyes and mouths; and S4, inputting numbers of lowering the head, closing the eyes andyawning per minute into a regression model, optimizing parameters by adopting a gradient descent algorithm, and finally outputting fatigue degree. The driving fatigue degree detection regression model disclosed by the invention carries out statistics on frequencies of facial features and realizes fatigue detection by establishing a regression model.

Description

technical field [0001] The invention relates to the technical field of establishing fatigue degree linear regression based on neural network recognition results, in particular to a driving fatigue degree detection regression model based on double network results. Background technique [0002] Accident prevention has always been one of the research hotspots, and in the past decade, many researchers have been working hard to develop driver monitoring systems using different techniques. The precise detection technology is based on the driver's physiological phenomena like brain waves, heart rate, pulse rate and respiration. These technologies based on human physiological responses are achieved in two ways: measuring changes in physiological signals such as brain waves, heart rate, and eye blinks and measuring changes in human posture, such as nodding gestures, recognizing the driver's head and The open / closed state of the eyes. The fatigue data generated by this technology is...

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04
CPCG06V40/165G06V40/172G06N3/045G06F18/214Y02T10/40
Inventor 张中牛雷
Owner 合肥湛达智能科技有限公司
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