Seat belt slack state detection method and apparatus, device, and storage medium

Abstract

Disclosed in embodiments of the present invention are a seat belt slack state detection method and apparatus, a device, and a storage medium. The method comprises: acquiring an in-vehicle occupant image of a vehicle, and determining a target region of interest in the in-vehicle occupant image, which spans from above the shoulder of an in-vehicle occupant to a seat belt storage position and comprises a seat belt; determining a seat belt wearing boundary line on the basis of the seat belt image in the target region of interest, and determining an included angle between the seat belt wearing boundary line and a target horizontal line; and determining a seat belt slack state on the basis of the included angle. In the method, a seat belt slack state is detected on the basis of a region of interest to remind an in-vehicle occupant to properly wear a seat belt, thereby enhancing the protective function of the seat belt.

Description

Method, device and equipment for detecting belt slack state and storage medium TECHNICAL FIELD

[0001] The present application relates to the technical field of image processing, and in particular to a method, device and equipment for detecting belt slack state and storage medium. BACKGROUND

[0002] In vehicle driving, wearing a safety belt can provide protection for people in the vehicle in emergency situations and reduce the risk of injury in accidents.

[0003] In the prior art, the wearing of a safety belt is usually only judged whether the safety belt is buckled, and the state of wearing a safety belt is not detected. In practice, when the safety belt is too loose, the protection effect of the safety belt in an emergency situation will be greatly reduced. Therefore, it is necessary to detect the slack state of the safety belt to urge people in the vehicle to wear safety belts in a standard manner.

[0004] SUMMARY

[0005] The present application provides a method, device and equipment for detecting belt slack state and storage medium to detect the slack state of the safety belt and urge people in the vehicle to wear safety belts in a standard manner.

[0006] According to an aspect of the present application, a method for detecting belt slack state is provided, which comprises:

[0007] Obtaining an image of a person in a vehicle and determining a target region of interest in the image of the person in the vehicle from above the shoulder of the person in the vehicle to a safety belt storage position and containing the safety belt;

[0008] Determining a safety belt wearing boundary line according to the safety belt image in the target region of interest and determining an included angle between the safety belt wearing boundary line and a target horizontal line;

[0009] Determining a safety belt slack state according to the included angle.

[0010] Optionally, obtaining an image of a person in a vehicle and determining a target region of interest in the image of the person in the vehicle from above the shoulder of the person in the vehicle to a safety belt storage position and containing the safety belt comprises:

[0011] Obtaining an image of a person in a vehicle and performing image segmentation on the image of the person in the vehicle to obtain a person-in-vehicle region and a safety belt region;

[0012] Extracting a region in the safety belt region that is non-overlapping with the person-in-vehicle region as the target region of interest.

[0013] Optionally, the safety belt wearing boundary line is determined according to a safety belt in the target region of interest, and the method comprises the following steps:

[0014] According to the safety belt image in the target region of interest, a safety belt storage point and a boundary point of a lower edge of the safety belt are determined.

[0015] The safety belt storage point and the boundary point are connected to obtain the safety belt wearing boundary line.

[0016] Optionally, the target horizontal line is in the same direction as the transverse safety belt.

[0017] According to the included angle, the safety belt slack state is determined, and the method comprises the following steps:

[0018] When the included angle is less than a preset angle, it is determined that the safety belt state is in a tight state; otherwise, it is determined that the safety belt state is in a slack state.

[0019] Optionally, an in-vehicle personnel image of the vehicle is obtained, and a target region of interest containing a safety belt from above a shoulder of an in-vehicle personnel to a safety belt storage position is determined, and the method comprises the following steps:

[0020] An in-vehicle personnel image of the vehicle is obtained, and a target region of interest containing a safety belt from above a shoulder of an in-vehicle personnel to a safety belt storage position is determined by a geometric drawing method.

[0021] Optionally, the method further comprises:

[0022] When it is determined that the state of the in-vehicle personnel wearing the safety belt is in a slack state, a safety belt wearing abnormality alarm is performed.

[0023] According to another aspect of the present application, a safety belt slack state detection device is provided, and the device comprises:

[0024] A target region of interest determination module is configured to obtain an in-vehicle personnel image of the vehicle, and determine a target region of interest containing a safety belt from above a shoulder of an in-vehicle personnel to a safety belt storage position.

[0025] An included angle determination module is configured to determine a safety belt wearing boundary line according to a safety belt image in the target region of interest, and determine an included angle between the safety belt wearing boundary line and a target horizontal line.

[0026] A safety belt slack state determination module is configured to determine a safety belt slack state according to the included angle.

[0027] According to another aspect of the present application, an electronic device is provided, and the electronic device comprises:

[0028] At least one processor; and

[0029] a memory communicatively connected with the at least one processor; wherein,

[0030] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the safety belt slack condition detection method according to any one of the embodiments of the present application.

[0031] According to another aspect of the present application, a computer readable storage medium is provided, which stores computer instructions for enabling a processor to implement the safety belt slack condition detection method according to any one of the embodiments of the present application when executed by the processor.

[0032] According to another aspect of the present application, a computer program product is provided, which comprises a computer program for implementing the safety belt slack condition detection method according to any one of the embodiments of the present application when executed by a processor.

[0033] The technical scheme of the embodiments of the present application acquires the image of the person in the vehicle, and determines the target region of interest in the image of the person in the vehicle from the shoulder of the person in the vehicle to the safety belt storage position and containing the safety belt. According to the safety belt image in the target region of interest, the safety belt wearing boundary line is determined, and the included angle between the safety belt wearing boundary line and the target horizontal line is determined. According to the included angle, the safety belt slack condition is determined, which solves the detection problem of the safety belt slack condition. The method detects the slack condition of the safety belt based on the region of interest, so as to urge the person in the vehicle to wear the safety belt regularly and enhance the protection function of the safety belt.

[0034] It should be understood that the content described in this part is not intended to identify the key or important features of the embodiments of the present application, nor is it used to limit the scope of the present application. Other features of the present application will become apparent from the following description. BRIEF DESCRIPTION OF DRAWINGS

[0035] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced as follows. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0036] Fig. 1 is a flowchart of a safety belt slack condition detection method according to an embodiment of the present application;

[0037] Fig. 2 is a schematic diagram of a target region of interest according to an embodiment of the present application;

[0038] Fig. 3 is a schematic diagram of a seat belt slackness detection according to an embodiment of the present application;

[0039] Fig. 4 is a flow chart of a seat belt slackness detection method according to an embodiment of the present application;

[0040] Fig. 5 is a schematic diagram of determining a target region of interest based on a geometric drawing method according to an embodiment of the present application;

[0041] Fig. 6 is a schematic diagram of a seat belt slackness detection device according to an embodiment of the present application;

[0042] Fig. 7 is a schematic diagram of an electronic device implementing a seat belt slackness detection method according to an embodiment of the present application. DETAILED DESCRIPTION

[0043] In order to make the personnel in the technical field better understand the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor should belong to the scope of protection of the present application.

[0044] It should be noted that the terms "first", "second", and the like in the specification and claims of the present application and the above-described drawings are used to distinguish similar objects, and do not necessarily indicate a specific order or a chronological sequence. It should be understood that the data thus used can be interchanged under appropriate circumstances, so that the embodiments of the present application described herein can be implemented in an order other than that illustrated or described herein. In addition, the terms "include" and "have" and any variations thereof are intended to cover non-exclusive inclusion, for example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but can include other steps or units not clearly listed or inherent to the process, method, product or device.

[0045] Embodiment One

[0046] Fig. 1 is a flow chart of a seat belt slackness detection method according to an embodiment of the present application. The present embodiment can be applied to the case of detecting the seat belt wearing slackness of a person in a vehicle. The method can be executed by a seat belt slackness detection device, which can be realized in the form of hardware and / or software. The seat belt slackness detection device can be configured in an electronic device such as a vehicle controller or a vehicle processor, etc. As shown in Fig. 1, the method comprises:

[0047] In step 110, an in-vehicle personnel image of the vehicle is acquired, and a target region of interest is determined in the in-vehicle personnel image, which is from above the shoulders of the in-vehicle personnel to a safety belt storage position and contains the safety belt.

[0048] In the embodiments of the present application, the in-vehicle personnel image can be an image of a driver or a passenger in the vehicle. The in-vehicle personnel image can include an image of the upper body and the abdominal region of the personnel. The in-vehicle personnel image can be captured by a camera, a video recorder or other imaging device. Specifically, to improve the accuracy of safety belt wearing detection, a camera with a resolution greater than a preset resolution can be used to capture real-time images of personnel inside the vehicle. For example, if the safety belt wearing status of the driver or the front passenger is to be detected, the camera can be installed at the position of the instrument panel or the A-pillar of the vehicle. If the safety belt wearing status of the rear passengers is to be detected, the camera can be installed at the position of the B-pillar or the interior roof of the vehicle. The specific number of cameras can be adjusted according to the actual safety belt wearing detection requirements.

[0049] To further improve the accuracy of safety belt wearing detection in different environments, a camera with adaptive exposure adjustment function can be used. The camera with adaptive exposure adjustment function can obtain clear in-vehicle personnel images under different lighting conditions, such as sunlight, shadow or night, thereby improving the accuracy of safety belt slackness detection.

[0050] Before obtaining the in-vehicle personnel image for processing, the in-vehicle personnel image can also be pre-processed. The pre-processing operation includes but is not limited to image denoising, brightness adjustment and contrast enhancement. Through the image pre-processing operation, the image quality can be improved, and a more accurate visual basis can be provided for the detection of the slackness of the safety belt.

[0051] In the embodiments of the present application, the target region of interest is a region that can represent the slackness of the safety belt. The inventors have found in actual research that when the safety belt is slack, it is usually first shown near the fixed point above the safety belt, i.e. the storage position. Since the human body region has a certain influence on the detection of the slackness of the safety belt, for example, the clothing of the human body blocks. There will be a high deviation in the detection of the slackness of the safety belt by the human body region. Therefore, in the embodiments of the present application, the region from above the shoulders of the in-vehicle personnel to the safety belt storage position and containing the safety belt in the in-vehicle personnel image is taken as the target region of interest. FIG. 2 is a schematic diagram of a target region of interest according to an embodiment of the present application. As shown in FIG. 2, the purple region in the figure is the target region of interest. The purple region and the yellow region together constitute the safety belt region.

[0052] In a specific application, when detecting the safety belt loosening state of the driver, the region above the left shoulder of the driver (the right shoulder in the image of the person in the vehicle) to the safety belt storage position and containing the safety belt can be taken as the target region of interest. When detecting the safety belt loosening state of the co-driver, the region above the right shoulder of the co-driver (the left shoulder in the image of the person in the vehicle) to the safety belt storage position and containing the safety belt can be taken as the target region of interest.

[0053] There can be various ways to extract the target region of interest. For example, the image segmentation technology can be used to segment the target region of interest in the image. Alternatively, the target region of interest can be extracted by drawing in the image of the person in the vehicle. Alternatively, the target region of interest can be obtained by image cutting in the image of the person in the vehicle.

[0054] The safety belt loosening state can be evaluated by extracting the target region of interest, which can avoid the influence of the background region and the human body region on the detection accuracy and improve the detection reliability.

[0055] In step 120, the safety belt wearing boundary line is determined according to the safety belt image in the target region of interest, and the included angle between the safety belt wearing boundary line and the target horizontal line is determined.

[0056] The safety belt image in the target region of interest can be obtained in various ways according to the determination method of the target region of interest. For example, when the target region of interest is obtained by image segmentation, the safety belt image is consistent with the target region of interest. For another example, when the target region of interest is obtained by geometric drawing or coordinate positioning, the safety belt image in the target region of interest can be obtained by a deep learning model, image segmentation technology, or background calibration.

[0057] The safety belt wearing boundary line can be the lower boundary line, the upper boundary line, or the middle line of the safety belt when the safety belt is worn. For example, FIG. 3 is a safety belt loosening state detection diagram provided by an embodiment of the present application. As shown in FIG. 3, the purple region is the target region of interest, and the green line is the safety belt wearing boundary line.

[0058] In an optional embodiment of the present application, the safety belt wearing boundary line is determined according to the safety belt in the target region of interest, including: determining the safety belt storage point and the boundary point of the lower edge of the safety belt according to the safety belt image in the target region of interest; and connecting the safety belt storage point and the boundary point to obtain the safety belt wearing boundary line.

[0059] As shown in FIG. 3, the yellow dot is a seat belt storage point, and the white dot is a boundary point of the lower edge of the seat belt. In actual application, when detecting the seat belt relaxation state of the person on the driver's side, the rightmost point in the seat belt image can be determined as the boundary point of the lower edge of the seat belt. When detecting the seat belt relaxation state of the person on the co-driver's side, the leftmost point in the seat belt image can be determined as the boundary line of the lower edge of the seat belt. As shown in FIG. 3, the green line obtained by connecting the seat belt storage point and the boundary point is the seat belt wearing boundary line.

[0060] As shown in FIG. 3, the target horizontal line can be a line extending in the transverse seat belt direction. Specifically, when detecting the seat belt relaxation state of the person on the driver's side, the target horizontal line can be a line extending to the right in the horizontal direction, that is, the red line in FIG. 3. When detecting the seat belt relaxation state of the person on the co-driver's side, the target horizontal line can be a line extending to the left in the horizontal direction.

[0061] Step 130: determining the seat belt relaxation state according to the included angle.

[0062] There can be various cases for determining the seat belt relaxation state according to the included angle. For example, as shown in FIG. 3, when the target horizontal line is a line extending in the transverse seat belt direction, when the included angle is less than a preset angle, it is determined that the seat belt state is in a tight state; otherwise, it is determined that the seat belt state is in a relaxation state.

[0063] Alternatively, when the target horizontal line is a line extending in the opposite direction of the transverse seat belt direction, when the included angle is greater than a preset angle, it is determined that the seat belt state is in a tight state; otherwise, it is determined that the seat belt state is in a relaxation state. The preset angle can be any value greater than 90 degrees.

[0064] In an optional embodiment of the embodiment of the present application, the target horizontal line is in the same direction as the transverse seat belt; and determining the seat belt relaxation state according to the included angle includes: when the included angle is less than a preset angle, determining that the seat belt state is in a tight state; otherwise, determining that the seat belt state is in a relaxation state.

[0065] The preset angle can be any value less than or equal to 90 degrees. For example, the preset angle can be any value in the range of 45 degrees to 90 degrees. For example, the preset angle can be 90 degrees, 60 degrees, or 45 degrees, etc.

[0066] On the basis of the above-mentioned embodiment, optionally, the method further includes: when it is determined that the state of the seat belt worn by the person in the vehicle is in a relaxation state, performing abnormal seat belt wearing alarm. The abnormal alarm can be audio and / or visual prompt alarm. In the embodiment of the present application, the abnormal alarm can be a real-time feedback mechanism, thereby accurately and timely prompting the person in the vehicle to wear the seat belt and ensuring driving safety.

[0067] The technical scheme of the embodiment acquires the in-vehicle personnel image of the vehicle, and determines a target region of interest in the in-vehicle personnel image from above the shoulders of the in-vehicle personnel to the safety belt storage position and containing the safety belt; determines the safety belt wearing boundary line according to the safety belt image in the target region of interest, and determines the included angle between the safety belt wearing boundary line and a target horizontal line; and determines the safety belt slack state according to the included angle, thereby solving the problem of detecting the safety belt slack state. The method detects the safety belt slack state based on the region of interest, so as to urge the in-vehicle personnel to wear the safety belt in a standard manner and enhance the protection function of the safety belt.

[0068] Embodiment Two

[0069] FIG. 4 is a flowchart of a safety belt slack state detection method according to Embodiment Two of the present application. The technical scheme in this embodiment is a further refinement of the above technical scheme, and can be combined with each optional scheme in one or more of the above embodiments. As shown in FIG. 4, the method comprises the following steps:

[0070] In step 410, the in-vehicle personnel image of the vehicle is acquired, and a target region of interest in the in-vehicle personnel image from above the shoulders of the in-vehicle personnel to the safety belt storage position and containing the safety belt is determined.

[0071] In an optional implementation of the embodiment of the present application, acquiring the in-vehicle personnel image of the vehicle and determining a target region of interest in the in-vehicle personnel image from above the shoulders of the in-vehicle personnel to the safety belt storage position and containing the safety belt comprises: acquiring the in-vehicle personnel image of the vehicle, and performing image segmentation on the in-vehicle personnel image of the vehicle to obtain an in-vehicle personnel region and a safety belt region; and extracting a region in the safety belt region that is non-overlapping with the in-vehicle personnel region as the target region of interest.

[0072] The manner of image segmentation is not specifically limited in the embodiment of the present application. For example, the safety belt can be segmented in the image by a deep learning model or a traditional image processing manner. For example, a semantic segmentation model such as hrnet can be trained to segment the in-vehicle personnel region and the safety belt region. The segmentation accuracy can be pixel-level. Image segmentation by a deep learning model can consider various personnel body types, improve the safety belt segmentation accuracy, and further improve the applicability and robustness of safety belt slack state detection. When performing image segmentation, in addition to segmenting the in-vehicle personnel region and the safety belt region, the background region can also be segmented.

[0073] Before the semantic segmentation model is trained, an in-vehicle personnel image of a vehicle can be collected, and image labeling can be performed to generate a semantic segmentation model training sample. The image labeling can label an in-vehicle personnel region and a seat belt region in the in-vehicle personnel image. In the image labeling, a background region can also be labeled. In the embodiment of the present application, through image labeling, advanced image processing or deep learning technology can be used to accurately segment and identify the in-vehicle personnel region and the seat belt region, thereby providing a basis for judging the correctness of the seat belt slack state.

[0074] As shown in FIG. 2, the black region is a background region, and the blue region is an in-vehicle personnel region. The yellow region and the purple region together constitute a seat belt region. The purple region is a non-overlapping region of the seat belt region and the in-vehicle personnel region, i.e., a target region of interest.

[0075] Through the image segmentation technology, the target region of interest can be extracted, which can best represent the seat belt slack state, thereby improving the detection reliability of the seat belt slack state.

[0076] In an optional embodiment of the present application, the target region of interest from the top of the in-vehicle personnel shoulder to the seat belt storage position and containing the seat belt in the in-vehicle personnel image of the vehicle is obtained, and the target region of interest is determined by a geometric drawing method.

[0077] The geometric drawing method is used to obtain the target region of interest from the top of the in-vehicle personnel shoulder to the seat belt storage position and containing the seat belt in the in-vehicle personnel image. The specific geometric drawing method is not limited in the embodiment of the present application.

[0078] FIG. 5 is a schematic diagram of determining a target region of interest based on a geometric drawing method according to the second embodiment of the present application. As shown in FIG. 5, a diagonal seat belt preset line (red line in FIG. 5) is determined according to the preset start and end points of the seat belt in the in-vehicle personnel image, and a first circular point is determined on the perpendicular bisector (green line in FIG. 5) of the diagonal seat belt preset line. The diagonal seat belt preset line is determined according to the preset start and end points of the seat belt in the in-vehicle personnel image, and the first circular point is determined on the perpendicular bisector of the diagonal seat belt preset line, which includes determining a point on the perpendicular bisector of the diagonal seat belt preset line as the first circular point (yellow point in FIG. 5) if the distance between the point and the diagonal seat belt preset line is half the length of the diagonal seat belt preset line.

[0079] Draw a first target circle with the first circle center as the center and the length between the first circle center and the start point or the end point in the preset start and end points as the radius. In the first target circle, determine a first arc line (the orange arc line shown in FIG. 5) formed by the preset start and end points. Determine a first region of interest formed by the oblique safety belt preset line and the first arc line.

[0080] In the image of the person in the vehicle, determine a transverse safety belt preset line (the blue line shown in FIG. 5) according to the preset end point of the seat safety belt, and determine a second circle end point (the green dot shown in FIG. 5) on the transverse safety belt preset line. Wherein, the second circle end point is determined on the transverse safety belt preset line, including: starting from the preset end point, determining the second circle end point at a certain distance on the transverse safety belt preset line. For example, the distance from the preset end point to the shoulder position of the person in the vehicle close to the preset start point can be used as a reference distance. Starting from the preset end point, the second circle end point is determined on the transverse safety belt preset line at an interval of the reference distance along the transverse safety belt direction.

[0081] Draw a second target circle with the preset end point as the center and the length between the preset end point and the second circle end point as the radius. According to the second target circle, the preset end point, the second circle end point, and the intersection of the second target circle and the oblique safety belt preset line, determine a second region of interest (i.e., the closed region formed by the red line, the blue line, and the purple arc line shown in FIG. 5).

[0082] In the first region of interest, extract a region that is non-overlapping with the second region of interest as a target region of interest, i.e., the yellow region shown in FIG. 5.

[0083] The target region of interest can be obtained by the geometric drawing method shown in FIG. 5. Further, in the target region of interest, the safety belt image can be determined, and the safety belt relaxation state detection can be performed. Since the target region of interest shown in FIG. 5 includes a non-safety belt region, the image extraction of the safety belt image needs to be further performed on the target region of interest shown in FIG. 5. The target region of interest obtained by the image segmentation method shown in FIG. 2 is the safety belt image, and no further image extraction is needed.

[0084] Step 420, determine the safety belt storage point and the boundary point of the lower edge of the safety belt according to the safety belt image in the target region of interest.

[0085] Step 430, connect the safety belt storage point and the boundary point to obtain a safety belt wearing boundary line, and determine the included angle between the safety belt wearing boundary line and a target horizontal line.

[0086] Wherein, the target horizontal line is in the same direction as the transverse safety belt.

[0087] Step 440, when the included angle is less than the preset angle, determining that the seat belt state is a tight state; otherwise, determining that the seat belt state is a slack state.

[0088] Step 450, when it is determined that the state of the seat belt worn by the vehicle occupant is a slack state, performing a seat belt wearing abnormality alarm.

[0089] The technical scheme of the embodiment of the application, by acquiring the vehicle occupant image of the vehicle, and determining the target region of interest in the vehicle occupant image from the shoulder of the vehicle occupant to the seat belt storage position and containing the seat belt; according to the seat belt image in the target region of interest, determining the seat belt storage point and the boundary point of the lower edge of the seat belt; connecting the seat belt storage point and the boundary point to obtain the seat belt wearing boundary line, and determining the included angle between the seat belt wearing boundary line and the target horizontal line; the target horizontal line and the transverse seat belt are in the same direction; when the included angle is less than the preset angle, determining that the seat belt state is a tight state; otherwise, determining that the seat belt state is a slack state; when it is determined that the state of the seat belt worn by the vehicle occupant is a slack state, performing a seat belt wearing abnormality alarm, solving the detection problem of the seat belt slack state, the method detects the seat belt slack state based on the region of interest, only evaluates the seat belt slack state from the part of the seat belt fixed in the vehicle to the human body, avoiding the influence of the human body on the detection result; through the included angle comparison method, the detection complexity can be reduced to the greatest extent, and the detection efficiency is improved; when the seat belt is slack, an abnormal alarm is performed, which can timely remind the driver to adjust, so that the seat belt can play the maximum protection role in an emergency, and the driving safety is significantly improved; the method improves the intelligent level of the seat belt use, reduces the false positive rate, and improves the system reliability; and the method is not limited to the detection of the seat belt slack state of a specific vehicle model, a specific position and a specific driving environment, has wide applicability and application prospect, and can promote the intelligent development of vehicles.

[0090] In the technical scheme of the embodiment of the application, the acquisition, storage and application of the user personal information (such as the vehicle occupant image) comply with the relevant legal regulations and do not violate the public order and good customs.

[0091] Embodiment three

[0092] FIG. 6 is a structural schematic diagram of a seat belt slack state detection device according to embodiment three of the application. As shown in FIG. 6, the device includes a target region of interest determination module 610, an included angle determination module 620 and a seat belt slack state determination module 630. Wherein:

[0093] The target region of interest determination module 610 is used for acquiring the vehicle occupant image of the vehicle, and determining the target region of interest in the vehicle occupant image from the shoulder of the vehicle occupant to the seat belt storage position and containing the seat belt;

[0094] The included angle determination module 620 is configured to determine a seat belt wearing boundary line according to the seat belt image in the target region of interest, and determine an included angle between the seat belt wearing boundary line and a target horizontal line.

[0095] The seat belt slack state determination module 630 is configured to determine a seat belt slack state according to the included angle.

[0096] Optionally, the target region of interest determination module 610 comprises:

[0097] The image segmentation unit is configured to acquire an in-vehicle personnel image of the vehicle, and perform image segmentation on the in-vehicle personnel image of the vehicle to obtain an in-vehicle personnel region and a seat belt region.

[0098] The first target region of interest determination unit is configured to extract a region that is non-overlapped with the in-vehicle personnel region in the seat belt region as the target region of interest.

[0099] Optionally, the included angle determination module 620 is specifically configured to:

[0100] determine a seat belt storage point and a boundary point of a lower edge of the seat belt according to the seat belt image in the target region of interest;

[0101] connect the seat belt storage point and the boundary point to obtain the seat belt wearing boundary line.

[0102] Optionally, the target horizontal line is in the same direction as the transverse seat belt.

[0103] The seat belt slack state determination module 630 comprises:

[0104] The seat belt slack state determination unit is configured to determine that the seat belt state is a tight state when the included angle is less than a preset angle, and otherwise, determine that the seat belt state is a slack state.

[0105] Optionally, the target region of interest determination module 610 comprises:

[0106] The second target region of interest determination unit is configured to acquire an in-vehicle personnel image of the vehicle, and determine a target region of interest above a shoulder of an in-vehicle personnel in the in-vehicle personnel image to a seat belt storage position and containing the seat belt through a geometric drawing manner.

[0107] Optionally, the device further comprises:

[0108] The abnormality alarm module is configured to perform a seat belt wearing abnormality alarm when it is determined that the state of the in-vehicle personnel wearing the seat belt is a slack state.

[0109] The safety belt slack state detection device provided by the embodiment of the present application can execute the safety belt slack state detection method provided by any of the embodiments of the present application, and has the corresponding function modules and beneficial effects of the execution method.

[0110] Embodiment Four

[0111] FIG. 7 shows a structural schematic diagram of an electronic device 10 that can be used to implement embodiments of the present application. The electronic device is intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular telephones, smart phones, wearable devices (e.g., headsets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be examples only, and are not intended to limit the implementations of the present application described and / or claimed in this document.

[0112] As shown in FIG. 7, the electronic device 10 includes at least one processor 11, and a memory, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., which is communicatively connected to the at least one processor 11, wherein the memory stores a computer program that can be executed by the at least one processor. The processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or loaded from the storage unit 18 into the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0113] A plurality of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, a loudspeaker, etc.; a storage unit 18, such as a magnetic disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network, such as the Internet, and / or various telecommunication networks.

[0114] The processor 11 can be various general and / or special purpose processing components having processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 performs various methods and processes described above, such as the seat belt slackness state detection method.

[0115] In some embodiments, the seat belt slackness state detection method can be implemented as a computer program tangibly embodied in a computer readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded onto the RAM 13 and executed by the processor 11, one or more steps of the seat belt slackness state detection method described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to perform the seat belt slackness state detection method by any other appropriate means, such as by means of firmware.

[0116] Various implementations of the systems and techniques described above can be realized in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a programmable logic device (PLD), a computer hardware, firmware, software, and / or combinations thereof. These various implementations can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0117] Computer programs used to implement the methods of the present application can be written in any combination of one or more programming languages. These computer programs can be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the computer program, when executed, implements the functions / acts specified in the flowcharts and / or block diagrams. The computer program can be executed entirely on a machine, partially on a machine, partially on a machine as a stand-alone software package, and partially on a machine or a remote machine or a server.

[0118] In the context of the present application, a computer-readable storage medium can be a tangible medium that can contain or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. A computer-readable storage medium can include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium can be a machine-readable signal medium. More specific examples of a machine-readable storage medium will include one or more lines of a program of instructions in a transitory signal, a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0119] To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user and a keyboard and a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the electronic device. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0120] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0121] The computing system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system, to solve the defects of large management difficulty and weak business scalability in traditional physical host and VPS service.

[0122] It should be understood that the various forms of flow shown above can be reordered, added to, or have steps deleted. For example, the steps described in the present application can be performed in parallel, in series, or in a different order, as long as the desired results of the technical solutions of the present application can be achieved, which are not limited herein.

[0123] The above detailed description does not constitute a limitation on the protection scope of the present application. Those skilled in the art should understand that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modifications, equivalent replacements and improvements made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A seat belt slack condition detection method characterized by, The method comprises the following steps: obtaining an in-vehicle personnel image of a vehicle, and determining a target region of interest in the in-vehicle personnel image, the target region of interest being from above a shoulder of an in-vehicle personnel to a seat belt storage position and containing a seat belt; determining a seat belt wearing boundary line according to a seat belt image in the target region of interest, and determining an included angle between the seat belt wearing boundary line and a target horizontal line; determining a seat belt slack state according to the included angle.

2. The method of claim 1, wherein, The method comprises the following steps: obtaining an in-vehicle personnel image of a vehicle, and performing image segmentation on the in-vehicle personnel image of the vehicle to obtain an in-vehicle personnel region and a seat belt region; extracting a region in the seat belt region that is non-overlapping with the in-vehicle personnel region as a target region of interest.

3. The method of claim 1, wherein, The method comprises the following steps: determining a seat belt wearing boundary line according to a seat belt in the target region of interest, which comprises the following steps: determining a seat belt storage point and a boundary point of a lower edge of the seat belt according to a seat belt image in the target region of interest; 4. The method of claim 1, wherein, connecting the seat belt storage point and the boundary point to obtain the seat belt wearing boundary line. The target horizontal line is in the same direction as the transverse seat belt. The method comprises the following steps: determining a seat belt slack state according to the included angle, which comprises the following steps:

5. The method of claim 1, wherein, when the included angle is less than a preset angle, determining that the seat belt state is a tight state; otherwise, determining that the seat belt state is a slack state.

6. The method of claim 1, wherein, The method comprises the following steps: obtaining an in-vehicle personnel image of a vehicle, and determining a target region of interest in the in-vehicle personnel image, the target region of interest being from above a shoulder of an in-vehicle personnel to a seat belt storage position and containing a seat belt.

7. A seat belt slack condition detection apparatus characterized by comprising: The method further comprises the following steps: when it is determined that the state of the in-vehicle personnel wearing the seat belt is a slack state, performing a seat belt wearing abnormality alarm. The method comprises the following steps: a target region of interest determination module is configured to obtain an in-vehicle personnel image of a vehicle, and determine a target region of interest in the in-vehicle personnel image, the target region of interest being from above a shoulder of an in-vehicle personnel to a seat belt storage position and containing a seat belt; 8. An electronic device, comprising: an included angle determination module is configured to determine a seat belt wearing boundary line according to a seat belt image in the target region of interest, and determine an included angle between the seat belt wearing boundary line and a target horizontal line; a seat belt slack state determination module is configured to determine a seat belt slack state according to the included angle. The electronic device comprises: at least one processor; and 9. A computer-readable storage medium, characterized in that, a memory connected to the at least one processor in communication; wherein the memory stores a computer program that can be executed by the at least one processor, and the computer program is executed by the at least one processor to enable the at least one processor to execute the seat belt slack state detection method in any one of claims 1-6. The computer readable storage medium stores computer instructions for enabling the processor to execute the seat belt slack state detection method in any one of claims 1-6 when executed.

10. A computer program product comprising a computer program which, when executed by a processor, implements the seat belt slack condition detection method according to any one of claims 1-6.

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