Driver fatigue detection method based on neural network

A driver fatigue, neural network technology, applied in neural learning methods, biological neural network models, neural architectures, etc., can solve problems such as difficulty in implementation, lack of robustness, and the influence of driver driving habits, so as to reduce detection time. , Wide detection range and high detection accuracy

Active Publication Date: 2019-08-13
GUANGDONG UNIV OF TECH
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[0005] Chinese patent CN201711074403.0 discloses a driver fatigue detection system, namely a detection method based on three units, including head position detection unit, face scanning unit and pressure sensing unit, the face scanning unit detects the height difference between the upper eyelid and the lower eyelid, and obtains the driver's eye opening degree, and inputs the result into the control unit, and the pressure sensing unit is distributed on the outer circumference of the steering wheel, The detection results of the unit determine whether the driver is in a state of fatigue; however, the detection of pressure and head position is affected by the driving habits of the driver, and it is not r

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  • Driver fatigue detection method based on neural network
  • Driver fatigue detection method based on neural network
  • Driver fatigue detection method based on neural network

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[0068] Hereinafter, the present invention will be further described in detail with reference to the examples and drawings, but the implementation of the present invention is not limited thereto.

[0069] The present invention aims to provide a method for detecting driver fatigue with high accuracy and high scalability, which can protect the life safety of pedestrians and the property safety of other vehicles while helping the driver protect himself. The present invention proposes a driver fatigue detection framework based entirely on neural network, which consists of three parts, including face and key point detection, key region extraction, driver fatigue state detection; using face detection and face key point detection The combined cascaded neural network outputs the face and five key points, including eyes, nose and corners of the mouth, and then uses the region extraction algorithm to extract the mouth and eyes, and inputs the extraction results to the state recognition netwo...

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Abstract

The invention discloses a driver fatigue detection method based on a neural network. The driver fatigue detection method comprises the following steps: S1, acquiring an image by using an infrared camera; S2, using a multi-task cascade convolutional network algorithm to detect positions of the human face and the key points, and obtaining a detection model; S3, using a region extraction algorithm toextract eye and mouth region graphs according to the positions of the key points. The driver fatigue detection framework completely based on the neural network is provided, and the detection accuracyis higher. According to the algorithm, face detection and face key point detection tasks are combined, face parts and face key points can be output at the same time, and the detection time for detecting the faces first and then detecting the key points is shortened. The detection range is wider. The method is not limited to eyes and the mouth, the characteristics of calling, smoking and the likeare detected simultaneously, and expansibility is achieved.

Description

technical field [0001] The invention relates to the technical field of driver fatigue detection, in particular to a neural network-based driver fatigue detection method. Background technique [0002] Driver fatigue detection is very important for drivers, especially commercial vehicle drivers, because in traffic accidents, traffic accidents caused by driver errors account for about 74%; 25-30% of traffic accidents are caused by fatigue driving , and in major traffic accidents, 40% are caused by fatigue driving; 70% of drivers on the highway have fatigue driving experience, and the number of people smoking and watching mobile phones during driving is even greater. Statistics from the Traffic Management Bureau of the Ministry of Public Security show that in 2016, there were 50,400 truck-related road traffic accidents across the country, resulting in 25,000 deaths and 46,800 injuries, accounting for 30.5%, 48.23% and 27.81% of the total number of vehicle-related accidents. , m...

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Application Information

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IPC IPC(8): G06K9/00G06N3/04G06N3/08
CPCG06N3/08G06V40/171G06V40/172G06V40/161G06V20/597G06N3/045
Inventor 尹惠锋张伟
Owner GUANGDONG UNIV OF TECH
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