Driver fatigue detection apparatus and method

Abstract

The embodiment of the present application provides a kind of driver fatigue detection device and method, it is related to safe driving field, the embodiment of the present application can more accurately detect whether driver is in fatigue state, to ensure the safety of driving.The driver fatigue detection device includes: acquisition unit, for obtaining the driver image photographed from the left front of driver and the driver image photographed from the right front of driver;Detection unit, for judging whether driver is in fatigue state currently according to the driver image photographed from the left front of driver and the driver image photographed from the right front of driver.The present application is applied to driver fatigue detection.

Description

Technical Field

[0001] This invention relates to the field of safe driving, and more particularly to a driver fatigue detection device and method. Background Technology

[0002] Currently, with increasingly complex road conditions, driver safety is receiving more and more attention. Existing technology typically uses cameras to capture images of the driver's face, then generates information such as the driver's eye opening degree and blinking frequency based on these images, and uses this information to determine if the driver is fatigued. However, because the driver's position can change during driving, the captured facial images often fail to accurately capture the necessary information. Summary of the Invention

[0003] This invention provides a driver fatigue detection device and method, which can more accurately detect whether the driver is in a state of fatigue, thereby ensuring driving safety.

[0004] To achieve the above objectives, the embodiments of the present invention adopt the following technical solutions:

[0005] In a first aspect, embodiments of the present invention provide a driver fatigue detection device, which includes: an acquisition unit for acquiring a driver image taken from the driver's left front and a driver image taken from the driver's right front; and a detection unit for determining whether the driver is currently in a fatigued state based on the driver image taken from the driver's left front and the driver image taken from the driver's right front.

[0006] Secondly, embodiments of the present invention provide a driver fatigue detection method, comprising: acquiring a driver image taken from the driver's left front and a driver image taken from the driver's right front; and determining whether the driver is currently in a fatigued state based on the driver image taken from the driver's left front and the driver image taken from the driver's right front.

[0007] Thirdly, embodiments of the present invention provide a driver fatigue detection device, comprising: a processor, a memory, a bus, and a communication interface; the memory is used to store computer execution instructions, and the processor is connected to the memory via the bus. When the driver fatigue detection device is running, the processor executes the computer execution instructions stored in the memory to cause the driver fatigue detection device to perform the method provided in the second aspect.

[0008] Fourthly, embodiments of the present invention provide a computer storage medium including instructions that, when run on a driver fatigue detection device, cause the driver fatigue detection device to perform the method provided in the second aspect above.

[0009] The driver fatigue detection device and method provided by this invention captures driver images using camera devices positioned on the left and right sides in front of the driver, and then determines whether the driver is in a state of fatigued driving based on the captured driver images from the left and right sides. This improves detection accuracy by obtaining more precise parameters for fatigue detection from the driver images from the left and right sides, and avoids the problem of the camera failing to capture a complete image of the driver's face due to driver position movement, thus affecting detection accuracy. Attached Figure Description

[0010] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below.

[0011] Figure 1 This is one of the structural schematic diagrams of a driver fatigue detection device provided in an embodiment of the present invention;

[0012] Figure 2 This is a flowchart illustrating a driver fatigue detection method provided in an embodiment of the present invention.

[0013] Figure 3 This is a schematic diagram of another driver fatigue detection device provided in an embodiment of the present invention;

[0014] Figure 4 This is a schematic diagram of the structure of a detection unit 102 provided in an embodiment of the present invention;

[0015] Figure 5 This is a second schematic diagram of a driver fatigue detection device provided in an embodiment of the present invention;

[0016] Figure 6 This is the third schematic diagram of a driver fatigue detection device provided in an embodiment of the present invention. Detailed Implementation

[0017] The embodiments of the present invention will now be described with reference to the accompanying drawings.

[0018] In the embodiments of the present invention, the terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the terms "exemplary" or "for example" is intended to present the relevant concepts in a specific manner. Furthermore, in the description of the embodiments of the present invention, unless otherwise stated, "a plurality of" means two or more.

[0019] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to limit the invention. The singular forms “a” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms, unless the context clearly indicates otherwise.

[0020] It should be understood that although the terms first, second, third, etc., may be used in the embodiments of the present invention to describe various thresholds, signals, and instructions, these thresholds, signals, and instructions should not be limited to these terms. These terms are only used to distinguish thresholds, signals, and instructions from one another. For example, without departing from the scope of the embodiments of the present invention, a first threshold may also be referred to as a second threshold, and similarly, a second threshold may also be referred to as a first threshold, etc.

[0021] Depending on the context, the words “if” or “suppose” as used here can be interpreted as “when” or “in response to determination” or “in response to detection.” Similarly, depending on the context, the phrase “if determination” or “if detection (of the condition or event of the statement)” can be interpreted as “when determination” or “in response to determination” or “when detection (of the condition or event of the statement)” or “in response to detection (of the condition or event of the statement).”

[0022] First, the inventive concept of this invention will be introduced: In this invention, driver fatigue detection is performed by acquiring images of the driver from both sides of the driver's face, thereby avoiding the problem of low accuracy in fatigue detection caused by the camera's shooting angle failing to capture a complete image of the driver's face. Furthermore, this invention addresses the issue of how to obtain useful information during fatigue detection using images acquired from both sides of the driver's face by providing a complete method for extracting information from images.

[0023] Specifically, such as Figure 1 The diagram shown is a structural schematic of a driver fatigue detection device 10 provided in an embodiment of the present invention. The driver fatigue detection device specifically includes an acquisition unit 101 and a detection unit 102. The working process of the driver fatigue detection device 10 is as follows: Figure 2 The flowchart of the provided driver fatigue detection method is shown. This driver fatigue detection method specifically includes:

[0024] S201. Obtain an image of the driver taken from the driver's left front and an image of the driver taken from the driver's right front.

[0025] Specifically, in such Figure 1In the driver fatigue detection device shown, the acquisition unit 101 is used to acquire driver images taken from the driver's left front and driver images taken from the driver's right front.

[0026] Specifically, in this invention, it is considered that when a driver is driving, their head is generally facing forward. Even if the driver moves while driving, it is usually by turning their head to the left or right, and rarely involves significant head tilting or lowering. Therefore, in this embodiment of the invention, images of the driver's left and right front are selected for subsequent detection.

[0027] For example, such as Figure 3 As shown, the driver fatigue detection device 10 in this embodiment of the invention further includes two cameras: camera 1 and camera 2. The image acquisition step described above is achieved by using camera 1 and camera 2, which are respectively positioned to the left and right front of the driver.

[0028] It should be noted that the directions "left" and "right" referred to in the embodiments of the present invention can be the direction judged from the driver's angle when the driver is in a normal sitting posture, or the direction judged from the opposite direction of the driver's angle. In specific implementation, the positions of "left" and "right" can be interchanged, and the present invention does not impose any restrictions on this.

[0029] In one implementation, considering that the driver's eye image in the acquired driver image will change as the driver's head position changes, in order to further reduce the impact of driver displacement and camera device vibration on the detection results, this embodiment of the invention can acquire multiple driver images from the driver's left front and right front respectively, and then select the image that most accurately reflects the required information from the multiple driver images for the next detection processing. Therefore, step S201 in this invention may specifically include:

[0030] Acquire multiple images of the driver taken from the driver's left front and multiple images of the driver taken from the driver's right front; select the first image from the multiple images of the driver taken from the driver's left front; select the second image from the multiple images of the driver taken from the driver's right front.

[0031] The first image includes the driver image with the largest eye aspect ratio among multiple driver images taken from the driver's left front; the second image includes the driver image with the largest eye aspect ratio among multiple driver images taken from the driver's right front. The first and second images are used for subsequent step S202.

[0032] S202. Based on the driver images taken from the driver's left front and the driver's right front, determine whether the driver is currently fatigued.

[0033] Specifically, after acquiring images of the driver taken from the driver's left front and right front, relevant data for fatigue detection can be extracted from these two images to determine whether the driver is fatigued.

[0034] In one implementation, as Figure 1 In the driver fatigue detection device shown, the detection unit 102 is used to determine whether the driver is currently in a state of fatigue based on the driver image taken from the driver's left front and the driver image taken from the driver's right front.

[0035] Specifically, this invention considers that when determining whether a driver is fatigued, the aspect ratio of the driver's eyes can be used for judgment. Under normal circumstances, the aspect ratio of the eyes is around 5 / 2 (this can be statistically analyzed using big data). When a driver is fatigued, the eye width remains unchanged, but the eye height decreases. When the aspect ratio is below 5:1 (based on big data statistics, 5:1 here is only an estimate), the eyes are at the critical point of closing, and it can be determined that the person is fatigued. Based on the above detection method, in this embodiment of the invention, step S202 may specifically include:

[0036] S2021. Extract the driver's left eye image and the driver's right eye image with the largest aspect ratio from the first image and the second image.

[0037] The image of the driver taken from the driver's left front includes the first image, and the image of the driver taken from the driver's right front includes the second image.

[0038] In one implementation, the detection unit 102 in the driver fatigue detection device 10, such as Figure 4 As shown, it includes: an extraction subunit 1021 and a detection subunit 1022. Wherein:

[0039] Extraction subunit 1021 is used to extract the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio from the first image and the second image.

[0040] Furthermore, considering that when the driver's face is facing forward, the aspect ratio of the driver's right eye in the first image is R1 < R2 and L1 > L2 (for brevity and ease of reading, R1 and L1 will represent the aspect ratios of the driver's right eye in the first image taken from the driver's left front, and R2 and L2 will represent the aspect ratios of the driver's right eye in the second image taken from the driver's right front), it can be determined that in the first image taken by the left camera, the driver's left eye is facing the camera directly, and the right eye is offset from the camera; in the second image taken by the right camera, the driver's right eye is facing the camera directly, and the left eye is offset from the camera. Therefore, it can be determined that the left eye image in the first image is the driver's left eye image with the largest aspect ratio, and the right eye image in the second image is the driver's right eye image with the largest aspect ratio.

[0041] Then, as the driver's face turns to the left from directly in front of them at a certain angle, the left-eye image in the second image captured by the right-side camera gradually becomes proportionally distorted. This distortion continues until the aspect ratio of the right eye in the first image is greater than that in the second image (R1 ≥ R2), at which point the aspect ratio of the left and right eyes in the first image is more accurate than that in the second image. Therefore, at this point, the driver's left and right eye images in the first image are determined to be the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio, respectively.

[0042] Similarly, when the driver's face turns to the right from the front until L2≥L1, the driver's left and right eye images in the second image are determined as the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio, respectively.

[0043] Therefore, step S2021 specifically includes:

[0044] S2021a. Compare the aspect ratio R1 of the driver's right eye in the first image with the aspect ratio R2 of the driver's right eye in the second image, and compare the aspect ratio L1 of the driver's left eye in the first image with the aspect ratio L2 of the driver's left eye in the second image.

[0045] If R1 < R2 and L1 > L2, then execute S2021b-1; otherwise, if R1 ≥ R2, then execute S2021b-2, and if L2 ≥ L1, then execute S2021b-3.

[0046] S2021b-1. If R1 < R2 and L1 > L2, then determine that the driver's left eye image in the first image is the driver's left eye image with the largest aspect ratio; and the driver's right eye image in the second image is the driver's right eye image with the largest aspect ratio.

[0047] S2021b-2. If R1≥R2, then determine the driver's left and right eye images in the first image, which are the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio, respectively.

[0048] S2021b-3. If L2≥L1, then determine the driver's left and right eye images in the second image, which are the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio, respectively.

[0049] Specifically, in such Figure 4 In the detection unit 102 shown, the extraction subunit 1021 is specifically used to execute the contents of S2021a, S2021b-1, S2021b-2, and S2021b-3.

[0050] S2022. Based on the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio, determine whether the driver is currently fatigued.

[0051] Specifically, such as Figure 4 In the detection unit 102 shown, the detection subunit 1022 is used to determine whether the driver is currently in a state of fatigue based on the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio.

[0052] In this embodiment of the invention, it is considered that the aspect ratio of the driver's eyes in the captured images varies due to different shooting angles. Taking the driver's right eye as an example: when a person is looking straight ahead, the driver's right eye is facing the right-side camera and deviating from the left-side camera. Therefore, the aspect ratio of the driver's right eye in the image captured by the right-side camera is larger and more accurate; while the aspect ratio of the driver's right eye in the image captured by the left-side camera will be smaller, and the data will be distorted. Therefore, in order to obtain more realistic and effective eye images and improve the accuracy of fatigue detection, this embodiment of the invention selects the driver's left-eye image and the driver's right-eye image with the largest aspect ratio from the calculated data to determine whether the driver is in a state of fatigue.

[0053] For example, from the first image, the aspect ratio L1 of the driver's left eye is calculated to be 9 / 4, and the aspect ratio R1 of the right eye is 5 / 2; from the second image, the aspect ratio L2 of the driver's left eye is calculated to be 5 / 2, and the aspect ratio R2 of the right eye is 9 / 4. It can be seen that the aspect ratios of the driver's left eye and right eye include two sets of data: 5 / 2 and 9 / 4. Therefore, by selecting the right eye image from the first image and the left eye image from the second image, we can determine whether the driver is currently fatigued.

[0054] Specifically, in one implementation, the driver's left eye image and the driver's right eye image with the largest aspect ratio are selected to determine whether the driver is currently fatigued. This includes combining the right eye image from the first image and the left eye image from the second image into a single image, and then using the combined image for fatigue detection to determine whether the driver is currently fatigued.

[0055] Alternatively, in another implementation, the aspect ratio parameters of the driver's left and right eyes can be extracted from the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio. Then, the aspect ratio parameters of the driver's left and right eyes can be used to determine whether the driver is currently fatigued.

[0056] The driver fatigue detection device and method provided by this invention captures driver images using camera devices positioned on the left and right sides in front of the driver, and then determines whether the driver is in a state of fatigued driving based on the captured driver images from the left and right sides. This improves detection accuracy by obtaining more precise parameters for fatigue detection from the driver images from the left and right sides, and avoids the problem of the camera failing to capture a complete image of the driver's face due to driver position movement, thus affecting detection accuracy.

[0057] When using integrated units, Figure 5 A schematic diagram of another possible structure of the driver fatigue detection device involved in the above embodiments is shown. The driver fatigue detection device 30 includes a processing module 301, a communication module 302, and a storage module 303. The processing module 301 is used to control and manage the actions of the driver fatigue detection device 30; for example, the processing module 301 is used to support the driver fatigue detection device 30 in performing... Figure 2 The process is described in steps S201-S202. Communication module 302 supports communication between the driver fatigue detection device and other entities. Storage module 303 stores the program code and data of the driver fatigue detection device.

[0058] The processing module 301 can be a processor or controller, such as a central processing unit (CPU), a general-purpose processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA), or other programmable logic devices, transistor logic devices, hardware components, or any combination thereof. It can implement or execute the various exemplary logic blocks, modules, and circuits described in conjunction with the disclosure of this invention. The processor can also be a combination that implements computational functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc. The communication module 302 can be a transceiver, transceiver circuitry, or a communication interface, etc. The storage module 303 can be a memory.

[0059] When processing module 301 is as follows Figure 6 The processor shown, communication module 302 is Figure 6 The transceiver, storage module 303 is Figure 6 When the memory is used, the driver fatigue detection device involved in the embodiments of the present invention can be the following driver fatigue detection device 40.

[0060] Reference Figure 6 As shown, the driver fatigue detection device 40 includes: a processor 401, a transceiver 402, a memory 403, and a bus 404.

[0061] The processor 401, transceiver 402, and memory 403 are interconnected via bus 404. Bus 404 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus can be categorized as an address bus, data bus, control bus, etc. For ease of representation, only one thick line is used in the diagram, but this does not indicate that there is only one bus or one type of bus.

[0062] Processor 401 may be a general-purpose central processing unit (CPU), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits used to control the execution of the program of the present invention.

[0063] Memory 403 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital versatile optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. Memory may exist independently and be connected to the processor via a bus. Memory may also be integrated with the processor.

[0064] The memory 402 stores the application code for executing the present invention, and its execution is controlled by the processor 401. The transceiver 402 receives input from external devices, and the processor 401 executes the application code stored in the memory 403, thereby realizing the functions of each virtual unit in the driver fatigue detection device described in this embodiment of the invention.

[0065] It should be understood that, in various embodiments of the present invention, the sequence number of each process does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0066] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0067] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0068] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0069] In the above embodiments, implementation can be achieved entirely or partially through software, hardware, firmware, or any combination thereof. When implemented using software programs, implementation can be entirely or partially in the form of a computer program product. This computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium accessible to a computer or a data storage device containing one or more servers, data centers, etc., that can be integrated with the medium. The available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media (e.g., solid-state drives (SSDs)).

[0070] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A driver fatigue detection device, characterized in that, The acquisition unit is used to acquire images of the driver taken from the driver's left front and images of the driver taken from the driver's right front. The detection unit is used to determine whether the driver is currently fatigued based on the driver image taken from the driver's left front and the driver image taken from the driver's right front. The driver image taken from the driver's left front includes a first image, and the driver image taken from the driver's right front includes a second image; wherein, the detection unit specifically includes: an extraction subunit and a detection subunit; The extraction subunit is used to extract the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio from the first image and the second image; The detection subunit is used to determine whether the driver is currently in a state of fatigue based on the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio. The extraction subunit is specifically used for: Compare the aspect ratio R1 of the driver's right eye in the first image with the aspect ratio R2 of the driver's right eye in the second image, and compare the aspect ratio L1 of the driver's left eye in the first image with the aspect ratio L2 of the driver's left eye in the second image. If R1 < R2 and L1 > L2, then the driver's left eye image in the first image is determined to be the driver's left eye image with the largest aspect ratio; the driver's right eye image in the second image is determined to be the driver's right eye image with the largest aspect ratio; otherwise, If R1≥R2, then the driver's left and right eye images in the first image are determined as the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio, respectively. If L2≥L1, then the driver's left and right eye images in the second image are determined as the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio, respectively.

2. The driver fatigue detection device according to claim 1, characterized in that, The acquisition unit is specifically used to acquire multiple driver images taken from the driver's left front and multiple driver images taken from the driver's right front; and to select the first image from the multiple driver images taken from the driver's left front. The second image is selected from a plurality of driver images taken from the driver's right front; wherein the first image includes the driver image with the largest eye aspect ratio among a plurality of driver images taken from the driver's left front; and the second image includes the driver image with the largest eye aspect ratio among a plurality of driver images taken from the driver's right front.

3. A method for detecting driver fatigue, characterized in that, Acquire images of the driver taken from the driver's left front and images of the driver taken from the driver's right front; Based on the driver images taken from the driver's left front and the driver's right front, determine whether the driver is currently fatigued. The driver image taken from the driver's left front includes a first image, and the driver image taken from the driver's right front includes a second image; The step of determining whether the driver is currently fatigued based on the driver images taken from the driver's left front and the driver's right front specifically includes: Extract the driver's left eye image and the driver's right eye image with the largest aspect ratio from the first image and the second image; Based on the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio, determine whether the driver is currently in a state of fatigue; The step of extracting the driver's left eye image and the driver's right eye image with the largest aspect ratio from the first image and the second image specifically includes: Compare the aspect ratio R1 of the driver's right eye in the first image with the aspect ratio R2 of the driver's right eye in the second image, and compare the aspect ratio L1 of the driver's left eye in the first image with the aspect ratio L2 of the driver's left eye in the second image. If R1 < R2 and L1 > L2, then the driver's left eye image in the first image is determined to be the driver's left eye image with the largest aspect ratio; the driver's right eye image in the second image is determined to be the driver's right eye image with the largest aspect ratio; otherwise, If R1≥R2, then the driver's left and right eye images in the first image are determined as the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio, respectively. If L2≥L1, then the driver's left and right eye images in the second image are determined as the driver's left eye image with the largest aspect ratio and the driver's right eye image with the largest aspect ratio, respectively.

4. The driver fatigue detection method according to claim 3, characterized in that, The acquisition of driver images taken from the driver's left front and driver's right front specifically includes: Acquire multiple driver images taken from the driver's left front and multiple driver images taken from the driver's right front; select the first image from the multiple driver images taken from the driver's left front; select the second image from the multiple driver images taken from the driver's right front; wherein, the first image includes the driver image with the largest eye aspect ratio among the multiple driver images taken from the driver's left front; the second image includes the driver image with the largest eye aspect ratio among the multiple driver images taken from the driver's right front.

5. A driver fatigue detection device, characterized in that, include: Processor, memory, bus, and communication interface; The memory is used to store computer execution instructions. The processor is connected to the memory via a bus. When the driver fatigue detection device is running, the processor executes the computer execution instructions stored in the memory so that the driver fatigue detection device performs the driver fatigue detection method provided in claim 3 or 4.

6. A computer storage medium, characterized in that, The instruction, when it is operated on the driver fatigue detection device, causes the driver fatigue detection device to perform the driver fatigue detection method provided in claim 3 or 4.

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