CN112132040B - Vision-based safety belt real-time monitoring method, terminal equipment and storage medium - Google Patents
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Abstract
The invention relates to a safety belt real-time monitoring method based on vision, terminal equipment and a storage medium, wherein the method comprises the following steps: acquiring a real-time monitoring infrared image of a driver; positioning a human face and estimating the human face posture to obtain the human face position and the human face posture information; intercepting a safety belt detection area according to the face position and the face posture information; the belt detection area is input into a trained classifier to analyze the wearing condition of the belt. The method of the invention can reliably intercept the effective safety belt detection area under the complex driving scene of the driver; and the convolutional neural network model is optimized, so that the safety belt detection efficiency is further improved under the condition of ensuring the accuracy, and the real-time monitoring requirement on the condition that a driver wears the safety belt is met.
Description
Technical Field
The invention relates to the field of safety belt monitoring, in particular to a vision-based safety belt real-time monitoring method, terminal equipment and a storage medium.
Background
With the continuous popularization of automobiles, motor vehicle traffic rises year by year. Whether the driver wears the safety belt has a decisive influence on the casualty of the accident, but the potential safety hazard caused by improper use of the safety belt occurs due to the fact that part of the driver has loose safety consciousness.
In order to effectively analyze the situation that the driver wears the safety belt, an alarm is sent out in time to remind the driver. The method is to extract the edge of the safety belt by the traditional image processing method and judge whether the safety belt is fastened by combining the conditions of the inclination angle, the length, the parallel relation of the straight lines of the two edges and the like of the straight line of the safety belt, but the extraction of the edge of the safety belt is easily influenced by complex environmental background, illumination, wearing of the clothes of the driver and the like, so that the analysis of whether the safety belt is fastened is further influenced. Another method is to analyze in combination with a convolutional neural network model to determine whether to tie the belt by targeting the belt (the target may be the belt itself or a specific mark coated on the belt), segmenting or classifying the area of the driver's body below the face. The safety belt target positioning or dividing method is time-consuming to calculate, and the coating mark detection mode is poor in general applicability; and the body area of the driver below the face is judged, the characteristics of the safety belt cannot be highlighted due to the overlarge detection area, and the safety belt information above the shoulders cannot be fully utilized.
Disclosure of Invention
The invention aims to provide a safety belt real-time monitoring method based on vision, terminal equipment and a storage medium, so as to solve the problems. For this purpose, the invention adopts the following specific technical scheme:
according to an aspect of the present invention, there is provided a vision-based real-time safety belt monitoring method, including the steps of:
acquiring a real-time monitoring infrared image of a driver;
positioning a human face and estimating the human face posture to obtain the human face position and the human face posture information;
intercepting a safety belt detection area according to the face position and the face posture information;
the belt detection area is input into a trained classifier to analyze the wearing condition of the belt.
Further, the real-time monitoring infrared image is obtained through a fatigue monitoring infrared camera installed in the cab.
Further, the face position and face posture information includes face height H, width W, and upper left corner P 1 Lower right corner point P 2 And face pose estimation angles α, β, and γ, where α, β, and γ are pitch angle, yaw angle, and roll angle, respectively.
Further, the specific process of intercepting the safety belt detection area according to the face position and the face posture information is as follows:
calculating the upper left corner point P of the detection area of the safety belt according to the formula (1) tl The coordinates of the two points of the coordinate system,
wherein, (P) tl _x,P tl Y) is the point P tl Coordinates of (P) 1 _x,P 1 Y) and (P) 2 _x,P 2 Y) are respectively the upper left corner points P of the human face 1 And lower right corner point P 2 Coordinates of (c);
taking the face height H as a reference of the size of the safety belt detection area, and the size L=coef H of the safety belt detection area;
estimating angles alpha, beta and gamma according to the face gesture, and P is calculated according to formulas (2) and (3) tl The coordinates are corrected back to the standard state that the face is opposite to the camera,
wherein (P) c _x,P c Y) is the center coordinate of a rectangular frame of the face, (P) tl _x_roll,P tl Y roll) is corrected gamma and then the point P tl Is used for the purpose of determining the coordinates of (a),
wherein, (P) tl _x_r,P tl Y_r) is corrected to the alpha, beta and gamma postpoints P tl Is defined by the coordinates of (a).
Further, coef takes a value of 1.2.
Further, the classifier is realized by adopting an optimized convolutional neural network model, wherein the convolutional neural network model adopts a narrow and deep full convolutional network structure and comprises 9 convolutional layers and 1 output layer, 7 of the 9 convolutional layers are separable convolutional layers, the rest are common convolutional layers, and the convolutional layers adopt 3 multiplied by 3 and 1 multiplied by 1 convolutional kernels for characteristic extraction and weighting operation.
Further, the optimizing of the convolutional neural network model comprises the following steps: pruning a neuron structure with the convolution kernel weight parameter value smaller than a threshold value in a trained network model, performing iterative training on the pruned model to fine-tune model parameters, and finally performing merging operation on a convolution layer and a BatchNorm layer in the model according to a formula (4); the output result is a value rho of the set value for classifying and judging the wearing condition of the safety belt, when rho is more than a threshold value, the safety belt is judged not to be fastened, otherwise, the safety belt is fastened,
wherein y is conv As the operation result of the convolution layer, w conv And b conv The weight and bias of the convolution layer respectively; y is bn For the result of BatchNorm operation, lambda and mu are the scaling factor and offset of the BatchNorm layer, respectively, epsilon is a small value preventing the denominator from being 0, ex]And Var [ x ]]The sliding mean and the sliding variance of the BatchNorm layer are respectively;and->And the combined weight and bias value.
Further, the threshold is 0.65.
According to another aspect of the present invention there is also provided a terminal device comprising a memory, a processor and a computer program stored in said memory and executable on said processor, characterized in that said processor implements the steps of the method as described above when said computer program is executed.
According to a further aspect of the present invention there is also provided a computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method as described above.
By adopting the technical scheme, the invention has the beneficial effects that: the method of the invention can reliably intercept the effective safety belt detection area under the complex driving scene of the driver; and the convolutional neural network model is optimized, so that the safety belt detection efficiency is further improved under the condition of ensuring the accuracy, and the real-time monitoring requirement on the condition that a driver wears the safety belt is met.
Drawings
For further illustration of the various embodiments, the invention is provided with the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments and together with the description, serve to explain the principles of the embodiments. With reference to these matters, one of ordinary skill in the art will understand other possible embodiments and advantages of the present invention. The components in the figures are not drawn to scale and like reference numerals are generally used to designate like components.
FIG. 1 is a flow chart of a vision-based seat belt real-time monitoring method of the present invention;
FIG. 2 is a schematic illustration of an original seat belt detection zone;
fig. 3 is a schematic view of the corrected seat belt detection region.
Detailed Description
The invention will now be further described with reference to the drawings and detailed description.
With reference to fig. 1, a method for real-time monitoring of a safety belt based on vision is described, which is implemented on the basis of a driver fatigue detection device in order to reduce additional equipment costs. The method comprises the following steps:
s1, acquiring a real-time monitoring infrared image of a driver, and particularly acquiring the infrared image through a fatigue monitoring infrared camera arranged in a cab.
S2, positioning a human face and estimating the human face posture to obtain human face position and human face posture information, wherein the human face position and human face posture information comprises human face height H, width W and human face upper left corner P 1 Lower right corner point P 2 And face pose estimation angles α, β, and γ, where α, β, and γ are pitch angle, yaw angle, and roll angle, respectively.
S3, intercepting a safety belt detection area according to the face position and the face posture information. Specifically, based on the face position, the upper left corner point P of the belt detection area is calculated according to the formula (1) tl The coordinates are then set to the face height H as a reference for the size of the seat belt detection area, and the size l=coef×h of the seat belt detection area (coef takes a value of 1.2 in this embodiment), as shown in fig. 2.
Wherein (P) tl _x,P tl Y) is the point P tl Coordinates of (P) 1 _x,P 1 Y) and (P) 2 _x,P 2 Y) are respectively the upper left corner points P of the human face 1 And lower right corner point P 2 Is defined by the coordinates of (a).
To reduce the influence of camera safety angle, driver safety belt wearing mode, driver head action and the like on the interception of the detection area, estimating angles alpha, beta and gamma according to the human face posture, and converting P into a formula (2) and a formula (3) tl The coordinates are corrected back to the standard state that the face is opposite to the camera, as shown in fig. 3, so that the safety belt can be obviously represented in the detection area under different scenes, and the general applicability of the algorithm is improved.
Wherein (P) c _x,P c Y) is the center coordinate of a rectangular frame of the face, (P) tl _x_roll,P tl Y roll) is corrected gamma and then the point P tl Is defined by the coordinates of (a).
Wherein H and W are eachFace width and height, (P) tl _x_r,P tl Y_r) is corrected to the alpha, beta and gamma postpoints P tl Is defined by the coordinates of (a).
S4, inputting the detection area of the safety belt into a trained classifier to analyze the wearing condition of the safety belt.
The convolutional neural network has good learning ability for the diversity of the images of the safety belt caused by the complex wearing of the clothes of the driver, the background environment and the illumination influence, but the performance is slightly insufficient compared with the traditional image processing method due to the fact that the inference network has larger calculation amount. In order to meet the real-time performance of safety belt monitoring, the network layer needs to be simplified and optimized, and the calculation efficiency is improved as much as possible under the condition of meeting the classification accuracy. In this embodiment, the convolutional neural network model adopts a narrow and deep full convolutional network structure, as shown in table 1, and includes 9 convolutional layers and 1 sigmoid output layer, wherein 7 of the 9 convolutional layers are separable convolutional layers, and the rest are common convolutional layers. The convolution layers adopt 3 multiplied by 3 and 1 multiplied by 1 convolution kernels to carry out feature extraction and weighting operation, and a large-size convolution kernel receptive field effect is realized by overlapping small-size convolution kernels and using fewer parameters. In order to further improve the reasoning performance of the model, firstly pruning a neuron structure with the convolution kernel weight parameter value smaller than a threshold value in a trained network model, then carrying out iterative training on the pruned model to fine-tune model parameters, and finally carrying out merging operation on a convolution layer and a BatchNorm layer in the model according to a formula (4). The method reduces the calculated amount and the volume of the model, and further accelerates the model reasoning speed. The input parameter of the network model is 128×128×1, the output result is a value p of the seat belt wearing condition of classification judgment, when p > threshold value (in the embodiment, the threshold value is 0.65), the seat belt is judged to be unbuckled, otherwise, the seat belt is judged to be unbuckled.
Wherein y is conv As the operation result of the convolution layer, w conv And b conv The weight and bias of the convolution layer respectively; y is bn For the result of BatchNorm operation, lambda and mu are the scaling factor and offset of the BatchNorm layer, respectively, epsilon is a small value preventing the denominator from being 0, ex]And Var [ x ]]Sliding means and sliding variances learned by the BatchNorm layer respectively;and->And the combined weight and bias value.
Table 1 network architecture
According to the safety belt real-time monitoring method based on vision, firstly, according to the face position information and the face gesture, a driver can intercept an effective safety belt detection area all the time in a complex driving scene, so that the detection area is reduced, and the safety belt information is fully reflected; and then simplifying and optimizing the classified convolutional neural network model, so that the calculation amount of network reasoning is reduced while the detection precision of the safety belt is ensured, and further, the real-time monitoring of the safety belt is realized.
The invention further provides a terminal device, which can comprise a memory, a processor and a computer program stored in the memory and capable of running on the processor, wherein the processor executes the computer program to realize the steps in the embodiment of the method, such as steps S1-S4 shown in fig. 1.
Illustratively, the computer program may be partitioned into one or more modules/units that are stored in the memory and executed by the processor to perform the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing the specified functions, which instruction segments are used for describing the execution of the computer program in the terminal device.
The terminal device may be a computing device such as a desktop computer, a notebook computer, a palm computer, a cloud server, etc. The terminal device may include, but is not limited to, a processor, a memory. For example, it may also include input output devices, network access devices, buses, and the like.
The processor may be a central processing unit (Central Processing Unit, CPU), but may also be other general purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), off-the-shelf programmable gate arrays (Field-Programmable Gate Array, FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, or the like. The general purpose processor may be a microprocessor or the processor may be any conventional processor or the like, which is a control center of the terminal device, and which connects various parts of the entire terminal device using various interfaces and lines.
The memory may be used to store the computer program and/or module, and the processor may implement various functions of the terminal device by running or executing the computer program and/or module stored in the memory and invoking data stored in the memory. The memory may mainly include a storage program area and a storage data area, wherein the storage program area may store an operating system, an application program (such as a sound playing function, an image playing function, etc.) required for at least one function, and the like; the storage data area may store data (such as audio data, phonebook, etc.) created according to the use of the handset, etc. In addition, the memory may include high-speed random access memory, and may also include non-volatile memory, such as a hard disk, memory, plug-in hard disk, smart Media Card (SMC), secure Digital (SD) Card, flash Card (Flash Card), at least one disk storage device, flash memory device, or other volatile solid-state storage device.
Finally, the invention also provides a computer-readable storage medium storing a computer program, wherein the computer program when executed by a processor implements the steps of the method as described above, such as the steps S1-S4 shown in fig. 1.
The individual modules/units of the computer program may be stored in a computer-readable storage medium if implemented in the form of software functional units and sold or used as separate products. Based on such understanding, the present invention may implement all or part of the flow of the method of the above embodiment, or may be implemented by a computer program to instruct related hardware, where the computer program may be stored in a computer readable storage medium, and when the computer program is executed by a processor, the computer program may implement the steps of each of the method embodiments described above. Wherein the computer program comprises computer program code which may be in source code form, object code form, executable file or some intermediate form etc. The computer readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a U disk, a removable hard disk, a magnetic disk, an optical disk, a computer Memory, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), an electrical carrier signal, a telecommunications signal, a software distribution medium, and so forth. It should be noted that the computer readable medium contains content that can be appropriately scaled according to the requirements of jurisdictions in which such content is subject to legislation and patent practice, such as in certain jurisdictions in which such content is subject to legislation and patent practice, the computer readable medium does not include electrical carrier signals and telecommunication signals.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (7)
1. The real-time safety belt monitoring method based on vision is characterized by comprising the following steps of:
acquiring a real-time monitoring infrared image of a driver;
locating a face and performing face pose estimationObtaining face position and face posture information, wherein the face position and face posture information comprises face height H, width W and upper left corner of the faceRight lower corner->Is a face pose estimation angle +.>、/>And->Wherein->、/>And->Pitch angle, yaw angle and roll angle, respectively;
intercepting a safety belt detection area according to the face position and the face posture information, specifically comprising: calculating the upper left corner of the safety belt detection area according to the formula (1)The coordinates of the two points of the coordinate system,
（1）
wherein,for->Coordinates of->And->Respectively the upper left corner points of the facesAnd right lower corner->Coordinates of (c);
at the height of human faceAs a seat belt detection zone sizeLThe size of the seat belt detection area is +.>，/>A value of 1.2;
estimating an angle from a face pose、/>And->Will be +.>The coordinates are corrected back to the standard state that the face is opposite to the camera,
（2），
wherein the method comprises the steps ofIs the center coordinate of a rectangular frame of the human face, < >>For correction->Rear point->Is used for the purpose of determining the coordinates of (a),
（3），
wherein,for correction->Rear point->Coordinates of (c);
the belt detection area is input into a trained classifier to analyze the wearing condition of the belt.
2. The method of claim 1, wherein the real-time monitoring infrared image is obtained by a fatigue monitoring infrared camera mounted in the cab.
3. The method of claim 1, wherein the classifier is implemented using an optimized convolutional neural network model, wherein the convolutional neural network model uses a narrow and deep full convolutional network structure comprising 9 convolutional layers and 1 output layer, 9 volumesThe middle 7 of the lamination layers are separable convolution layers, the rest are common convolution layers, and the convolution layers adoptAnd->Is subjected to feature extraction and weighting operations.
4. The method of claim 3, wherein optimizing the convolutional neural network model comprises the steps of: pruning a neuron structure with the convolution kernel weight parameter value smaller than a threshold value in a trained network model, performing iterative training on the pruned model to fine-tune model parameters, and finally performing merging operation on a convolution layer and a BatchNorm layer in the model according to a formula (4); the output result is a value of the wearing condition of the classified judgment safety beltWhen->>If the threshold value is reached, the safety belt is judged not to be fastened, otherwise, the safety belt is fastened,
（4）
in the middle ofFor the convolution layer operation result, < >>And->The weight and bias of the convolution layer respectively; />Is the result of BatchNorm operation, +.>And->Scaling factor and offset of the BatchNorm layer, respectively,>is a smaller value preventing denominator from being 0,/->And->The sliding mean and the sliding variance of the BatchNorm layer are respectively; />And->And the combined weight and bias value.
5. The method of claim 4, wherein the threshold is 0.65.
6. Terminal device comprising a memory, a processor and a computer program stored in the memory and executable on the processor, characterized in that the processor implements the steps of the method according to any of claims 1-5 when the computer program is executed.
7. A computer readable storage medium storing a computer program, characterized in that the computer program when executed by a processor implements the steps of the method according to any one of claims 1-5.
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| CN113298000B (en) * | 2021-06-02 | 2022-10-25 | 上海大学 | Safety belt detection method and device based on infrared camera |
| CN113553938B (en) * | 2021-07-19 | 2024-05-14 | 黑芝麻智能科技(上海)有限公司 | Seat belt detection method, apparatus, computer device, and storage medium |
| CN113486835B (en) * | 2021-07-19 | 2024-06-28 | 黑芝麻智能科技有限公司 | Seat belt detection method, apparatus, computer device, and storage medium |
| CN113822197A (en) * | 2021-09-23 | 2021-12-21 | 南方电网电力科技股份有限公司 | Work dressing identification method and device, electronic equipment and storage medium |
| CN115123141A (en) * | 2022-07-14 | 2022-09-30 | 东风汽车集团股份有限公司 | Vision-based passenger safety belt reminding device and method |
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