Channel occupation monitoring method, device and equipment based on intelligent image recognition

Abstract

The invention discloses a channel occupation monitoring method, device and equipment based on intelligent image recognition. The method comprises the following steps: acquiring an acquired monitoring image from image acquisition equipment according to a preset acquisition interval time; judging each monitoring image according to a preset road occupation judgment rule to obtain a judgment result of whether each monitoring image is suspected to occupy a road; if the judgment result is that the monitoring image is suspected to occupy the lane, performing image adjustment on the monitoring image according to a preset image adjustment strategy to obtain a corresponding target image; performing type matching on the target image according to a preset type matching rule to obtain a corresponding lane occupation monitoring type; and generating a corresponding alarm instruction according to the road occupation monitoring type, and sending the alarm instruction to an alarm corresponding to the target image. According to the channel occupation monitoring method, the monitoring images can be periodically collected and judged, and if suspected channel occupation exists, image adjustment and type matching are carried out to obtain the channel occupation monitoring type; therefore, the channel occupation is intelligently identified, and the accuracy of channel occupation judgment is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image recognition, and particularly to a method, device and equipment for monitoring channel occupancy based on intelligent image recognition. Background Art

[0002] Inside a nuclear power plant, there are material transportation channels, personnel access channels, fire protection channels, etc. These channels need to be kept unoccupied. When objects are stacked in the channels to occupy them, it will affect the smooth transportation of materials or personnel in the nuclear power plant and affect the safety of the operation of the nuclear power plant. To avoid the channels being occupied for a long time without being effectively dredged in time, in the prior art, usually, monitoring personnel check whether there are objects occupying the images of each channel in the monitor to check whether the channels are occupied; however, due to the large number of channels in the nuclear power plant and the different wall colors and channel sizes in the channels, it causes the monitoring personnel to be unable to quickly identify a large number of channel images, affecting the accuracy of channel occupancy recognition. Therefore, the prior art methods cannot solve the problem of accurately identifying the channel occupancy situation in the nuclear power plant. Summary of the Invention

[0003] Embodiments of the present invention provide a method, device and equipment for monitoring channel occupancy based on intelligent image recognition, aiming to solve the problem in the prior art methods that the channel occupancy situation in the nuclear power plant cannot be accurately identified.

[0004] In a first aspect, embodiments of the present invention provide a method for monitoring channel occupancy based on intelligent image recognition. Among them, this method is applied to a management server, and the management server establishes a network connection with image acquisition devices and alarms set at each channel to realize the transmission of data information. The method includes:

[0005] Obtain the collected monitoring images from the image acquisition devices according to a preset acquisition interval time;

[0006] Judge each of the monitoring images according to a preset road occupation judgment rule to obtain a judgment result on whether each of the monitoring images is suspected of road occupation;

[0007] If the judgment result is suspected of road occupation, adjust the monitoring image according to a preset image adjustment strategy to obtain a corresponding target image;

[0008] Match the type of the target image according to a preset type matching rule to obtain a corresponding road occupation monitoring type;

[0009] Generate a corresponding alarm instruction according to the road occupation monitoring type and send it to the alarm corresponding to the target image.

[0010] Second aspect, an embodiment of the present invention further provides a channel occupancy monitoring device based on intelligent image recognition. The device is configured in a management server, and the management server establishes a network connection with image acquisition devices and alarms set at each channel to achieve data information transmission. The device is used to execute the channel occupancy monitoring method based on intelligent image recognition as described in the first aspect above. The device includes:

[0011] A monitoring image acquisition unit, configured to acquire the acquired monitoring images from the image acquisition device according to a preset acquisition interval time;

[0012] A judgment unit, configured to judge each of the monitoring images according to a preset road occupation judgment rule to obtain a judgment result on whether each of the monitoring images is suspected of road occupation;

[0013] A target image acquisition unit, configured to, if the judgment result is suspected of road occupation, perform image adjustment on the monitoring image according to a preset image adjustment strategy to obtain a corresponding target image;

[0014] A road occupation monitoring type acquisition unit, configured to perform type matching on the target image according to a preset type matching rule to obtain a corresponding road occupation monitoring type;

[0015] An alarm instruction sending unit, configured to generate a corresponding alarm instruction according to the road occupation monitoring type and send it to the alarm corresponding to the target image.

[0016] Third aspect, an embodiment of the present invention further provides a computer device. The device includes a processor, a communication interface, a memory, and a communication bus. The processor, communication interface, and memory complete mutual communication through the communication bus;

[0017] The memory is used to store a computer program;

[0018] The processor, when executing the program stored on the memory, implements the steps of the channel occupancy monitoring method based on intelligent image recognition as described in the first aspect above.

[0019] Fourth aspect, an embodiment of the present invention further provides a computer-readable storage medium, on which a computer program is stored. The computer program, when executed by a processor, implements the steps of the channel occupancy monitoring method based on intelligent image recognition as described in the first aspect above.

[0020] An embodiment of the present invention provides a method, apparatus, and device for monitoring channel occupancy based on intelligent image recognition. The method includes: obtaining captured monitoring images from an image capture device according to a preset acquisition interval time; judging each monitoring image according to a preset lane occupation judgment rule to obtain a judgment result on whether each monitoring image is suspected of lane occupation; if the judgment result is suspected of lane occupation, adjusting the monitoring image according to a preset image adjustment strategy to obtain a corresponding target image; performing type matching on the target image according to a preset type matching rule to obtain a corresponding lane occupation monitoring type; generating a corresponding alarm instruction according to the lane occupation monitoring type and sending it to an alarm corresponding to the target image. The above channel occupancy monitoring method can periodically collect monitoring images and judge whether there is a suspected lane occupation. If there is a suspected lane occupation, the monitoring image is further adjusted to obtain a target image and type matching is performed to obtain the corresponding lane occupation monitoring type and generate an alarm instruction; it can realize intelligent recognition of channel occupancy and greatly improve the accuracy of channel occupancy judgment. Description of the Drawings

[0021] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for description in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are some embodiments of the present invention. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0022] Figure 1 It is a flowchart of the method for monitoring channel occupancy based on intelligent image recognition provided by the embodiment of the present invention;

[0023] Figure 2 It is a schematic diagram of the application scenario of the method for monitoring channel occupancy based on intelligent image recognition provided by the embodiment of the present invention;

[0024] Figure 3 It is an application effect diagram of the method for monitoring channel occupancy based on intelligent image recognition provided by the embodiment of the present invention;

[0025] Figure 4 It is a schematic block diagram of the device for monitoring channel occupancy based on intelligent image recognition provided by the embodiment of the present invention;

[0026] Figure 5 It is a schematic block diagram of the computer device provided by the embodiment of the present invention. Detailed Embodiments

[0027] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0028] It should be understood that when used in this specification and the appended claims, the terms "comprising" and "including" indicate the presence of the described features, wholes, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, wholes, steps, operations, elements, components, and / or their combinations.

[0029] It should also be understood that the terms used in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. As used in the specification of the present invention and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an", and "the" are intended to include the plural forms.

[0030] It should be further understood that the term "and / or" used in the specification of the present invention and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0031] The embodiments of the present invention application provide a method for monitoring channel occupancy based on intelligent image recognition. This method is applied to the management server 10. The management server 10 executes the stored software program to implement the above-mentioned method for monitoring channel occupancy based on intelligent image recognition; the management server 10 can be a server-side configured in the machine room of a nuclear power plant for intelligently monitoring the channel occupancy situation. The management server 10 can be a server device such as a cluster server or a parallel processing server. Please refer to Figure 2 , as shown in the figure, in this management server, the management server establishes a network connection with the image acquisition device 20 and the alarm 30 set at each channel to realize the transmission of data information. The image acquisition device is configured at the channel and acquires images of a section of the channel; the alarm 30 is also a device configured at each channel of the nuclear power plant for alarm prompts, such as a loudspeaker.

[0032] As Figure 1 shown, this method includes steps S110 to S150.

[0033] S110. Obtain the acquired monitoring images from the image acquisition device according to the preset acquisition interval time.

[0034] The management server can collect monitoring images according to the collection interval. For example, the management server periodically sends collection instructions to each image collection device according to the collection interval. Each image collection device collects the monitoring images corresponding to the received collection instructions and sends them to the management server, and then the management server can receive the monitoring images collected by the image collection devices. If the collection interval can be set to 30 seconds, the management server can collect a monitoring image corresponding to an image collection device every 30 seconds. An image collection device is correspondingly set at each channel.

[0035] S120. Judge each of the monitoring images according to a preset road occupation judgment rule to obtain a judgment result on whether each of the monitoring images is suspected of road occupation.

[0036] Furthermore, judge the obtained monitoring images according to a preset road occupation judgment rule. The road occupation judgment rule is a specific rule for judging whether an occupied object is included in the channel corresponding to the monitoring image. Through judgment, the judgment results of each monitoring image can be obtained respectively, and the judgment results can reflect whether there is a suspected road occupation situation in the monitoring image.

[0037] In a specific embodiment, step S120 includes sub-steps: judge whether there is an abnormal color in each of the monitoring images according to the color parameter information in the road occupation judgment rule to obtain a corresponding abnormal color judgment result; extract the corresponding channel boundary contour from each of the monitoring images according to the boundary extraction parameter in the road occupation judgment rule; judge whether there is an abnormality in each of the channel boundary contours according to the boundary abnormality detection parameter in the road occupation judgment rule to obtain a corresponding boundary contour judgment result; if the abnormal color judgment result is that there is an abnormality or the boundary contour judgment result is that there is an abnormality, obtain a judgment result of being suspected of road occupation; if the abnormal color judgment result is that there is no abnormality and the boundary contour judgment result is that there is no abnormality, obtain a judgment result of not being suspected of road occupation.

[0038] Specifically, it is possible to determine whether there is an abnormal color in the monitoring image according to the color parameter information in the road occupation judgment rule. The colors of the ground, wall, and ceiling in the channel are fixed colors. Then, except for these fixed colors, if the monitoring image contains a color block that is significantly different from the ground color, it can be determined that the monitoring image is abnormal, and the abnormal color judgment result of the monitoring image is that there is an abnormal color. Since in actual situations, there may also be objects with colors close to the wall or ground stacked in the channel to occupy the channel, to further determine this occupation situation, the channel boundary contour can be extracted from the monitoring image according to the boundary extraction parameters. The channel boundary contour includes the contours between the ground and the wall, and between the wall and the ceiling in the channel. The channel boundary contour is judged whether there is an abnormality according to the boundary abnormality detection parameters, so as to obtain the boundary contour judgment result. The boundary contour judgment result also means whether the monitoring image is abnormal.

[0039] If the abnormal color judgment result of the monitoring image is that there is an abnormality, or the boundary contour judgment result of the monitoring image is that there is an abnormality, then the judgment result of the monitoring image is suspected of road occupation. If the abnormal color judgment result of the monitoring image is that there is no abnormality, and the boundary contour judgment result is also that there is no abnormality, then the judgment result of the monitoring image is not suspected of road occupation.

[0040] In a specific embodiment, the determining whether there is an abnormal color in each of the monitoring images according to the color parameter information in the road occupation judgment rule to obtain the corresponding abnormal color judgment result includes: obtaining the color interval corresponding to the monitoring image from the color parameter information; obtaining the pixel points in the monitoring image that are not within the color interval as abnormal pixel points; obtaining the connected domains corresponding to the abnormal pixel points in the monitoring image; and judging whether the area of each of the connected domains is greater than the area threshold in the color parameter information to determine whether there is an abnormal color in the monitoring image.

[0041] Specifically, the color interval corresponding to the monitoring image can be obtained from the color parameter information. The color parameter information contains the color intervals corresponding to each channel. Then, according to the image acquisition device of the channel corresponding to the monitoring image, the color interval corresponding to the monitoring image can be obtained from the color intervals contained in the color parameter information.

[0042] Further obtain the pixel points in the monitoring image that are not within the corresponding color range, and use the pixel points not within the pixel range as abnormal pixel points. If there are other abnormal pixel points in the surrounding neighboring positions of a certain abnormal pixel point, it indicates that this abnormal pixel point can be connected to other abnormal pixel points; if there are no other abnormal pixel points in the surrounding neighboring positions of a certain abnormal pixel point, it indicates that this abnormal pixel point is not connected to other abnormal pixel points; obtain the abnormal pixel points connected to a certain abnormal pixel point, and then determine again whether there are other abnormal pixel points among the connected abnormal pixel points. Through the connectivity relationship between abnormal pixel points, the abnormal pixel points can be combined to form a connected domain corresponding to the abnormal pixel points. It can be determined whether the area of each formed connected domain is greater than the area threshold in the color parameter information. If it is greater, it is determined that the monitoring image has abnormal colors. For example, the area threshold can be set to 25 pixel (pixels).

[0043] In a specific embodiment, the extracting the corresponding channel boundary contour from each of the monitoring images according to the boundary extraction parameters in the lane occupation judgment rule includes: calculating the contrast coefficient of each pixel point in the monitoring image; obtaining partial pixel points corresponding to the extraction ratio from the monitoring image according to the extraction ratio in the boundary extraction parameters and the contrast coefficient as alternative pixel points; performing binarization on the alternative pixel points to obtain a corresponding pixel contour; and screening from the pixel contour the pixel contour that matches the boundary reference direction according to the boundary reference direction in the boundary extraction parameters as the corresponding channel boundary contour.

[0044] When extracting the pixel boundary contour, the contrast coefficient of each pixel point in the monitoring image can be calculated first. The contrast coefficient is used to reflect the difference in comparison between a pixel point and other surrounding pixel points; the larger the contrast coefficient of a pixel point, the greater the difference in comparison between this pixel point and the surrounding pixel points. Specifically, the contrast coefficient can be calculated using formula (1):

[0045]

[0046] where B is the contrast coefficient of a certain pixel point in the calculated monitoring image, M is the number of pixel points in a circle surrounding this pixel point, S j is the pixel value of the j-th pixel point surrounding this pixel point, S0 is the pixel value of this pixel point; G is the number of pixel points surrounding this pixel point with an interval of one layer of pixel points, S k is the pixel value of the k-th pixel point among the outer pixels with an interval of one layer of pixel points from this pixel point, and e is the base of the natural logarithm. The pixel value is also the RGB value of a pixel point, and the RGB value is composed of the values of three color channels: red (R), green (G), and blue (B).

[0047] Further, pixel points are extracted according to the extraction ratio in the boundary extraction parameters. The extraction ratio is the ratio of the number of extracted pixel points to the number of pixel points in the monitoring image. For example, the extraction ratio can be set to 5%. Part of the pixel points with a relatively large contrast coefficient in the monitoring image can be obtained according to the extraction ratio as candidate pixel points.

[0048] Further, the candidate pixel points are binarized to obtain a pixel contour based on the candidate pixel points. In the pixel contour, the candidate pixel points are highlighted; that is, the pixel values of non-candidate pixel points in the image are set to white, and the pixel values of candidate pixel points are set to black.

[0049] The pixel contours can be screened according to the boundary reference direction in the boundary extraction parameters. Specifically, it can be first determined whether each pixel contour is a closed pixel contour. If the pixel contour is a closed pixel contour, it is screened out, and the non-closed pixel contours are retained. Further, the starting point, the midpoint, and the ending point are obtained from each non-closed pixel contour as marked pixel points. The coordinate positions of the marked pixel points are obtained, and the angle between the marked connection line between adjacent marked pixel points and the boundary reference direction is calculated. It is judged whether the angle is not greater than the preset angle range; if the angles corresponding to the marked connection lines between adjacent marked pixel points in the pixel contour are all within the angle range, it is determined that the pixel contour matches the boundary reference direction; otherwise, it is determined that the pixel contour does not match the boundary reference direction. According to the above method, all pixel contours that match the boundary reference direction can be obtained as the corresponding channel boundary contours.

[0050] In a specific embodiment, judging whether there is an abnormality in each of the channel boundary contours according to the boundary abnormality detection parameters in the lane occupation judgment rule to obtain a corresponding boundary contour judgment result includes: judging whether the extension directions of the channel boundary contours are the same; if the extension directions of two channel boundary contours are the same, the deviation coefficient between the two channel boundary contours with the same extension direction is obtained according to the basic parameters in the boundary abnormality detection parameters; it is judged whether the deviation coefficient of the two channel boundary contours with the same extension direction is within the deviation range in the boundary abnormality detection parameters to determine whether there is an abnormality in the channel boundary contour.

[0051] Judging whether there is an abnormality in the channel boundary contour, it can be judged whether the extension directions of the channel boundary contours are the same. Specifically, the angle between the channel boundary contour and the horizontal line can be obtained, and it is sequentially judged whether the extension directions are the same; if the difference in the angles between two channel boundary contours and the horizontal line is less than the angle threshold, it is determined that the extension directions of the two channel boundary contours are the same; otherwise, it is judged that the extension directions of the two channel boundary contours are different.

[0052] If the extension directions of the two channel boundary contours are the same, further obtain the deviation coefficient between the two channel boundary contours with the same extension direction according to the basic parameters in the boundary anomaly detection parameters. Specifically, the process of obtaining the deviation coefficient can be expressed by formula (2):

[0053]

[0054] L m is the minimum distance value between the two channel boundary contours with the same extension direction, θ is the angle difference between the two channel boundary contours with the same extension direction and the horizontal line, P is the calculated deviation coefficient, and J is the basic parameter.

[0055] Further determine whether the deviation coefficient of the channel boundary contour is within the deviation interval in the boundary anomaly detection parameters. If it is within the deviation interval, it indicates that there is no abnormal situation for the currently judged channel boundary contour; if it is not within the deviation interval, it indicates that there is a large range of discontinuity for the currently judged channel boundary contour, that is, there is an abnormal situation. At this time, the area between the two channel boundary contours currently being judged can be marked (for example, the area between the two channel boundary contours is color-replaced with a special color that is significantly different from the ground and the wall).

[0056] S130. If the judgment result is suspected of occupying the road, perform image adjustment on the monitoring image according to the preset image adjustment strategy to obtain the corresponding target image.

[0057] If the judgment result of the monitoring image is suspected of occupying the road, the monitoring image can be adjusted according to the image adjustment strategy. After adjusting a monitoring image, the corresponding target image can be obtained. If the judgment result of the monitoring image is not suspected of occupying the road, there is no need to perform subsequent processing on the monitoring image, and return to execute step S110.

[0058] In a specific embodiment, step S130 includes sub-steps: obtaining the adjustment mapping model corresponding to the monitoring image in the image adjustment strategy; performing mapping adjustment on the coordinate positions of each pixel point in the monitoring image according to the coordinate mapping relationship in the adjustment mapping model to obtain the corresponding target image.

[0059] The image adjustment strategy includes adjustment mapping models corresponding to each image acquisition device. The adjustment mapping models are generated according to the installation position of the image acquisition device in the channel and the orientation of the image acquisition device. The adjustment mapping models include the mapping relationship between the current position and orientation of the image acquisition device and the reference image. Since the installation positions and orientations of the image acquisition devices may vary, the image adjustment strategy includes multiple adjustment mapping models. An adjustment mapping model corresponding to the monitoring image determined to be suspected of occupying the road can be obtained. The adjustment mapping model includes the coordinate mapping relationship corresponding to each coordinate point in the monitoring image. The coordinate mapping relationship can map the coordinate position of a coordinate point to a coordinate position in the reference image. As Figure 3 shown, the adjustment mapping model corresponding to a certain monitoring image can map and adjust point A1 in the monitoring image to point A2 through the corresponding coordinate mapping relationship. Similarly, based on the coordinate mapping relationship, points B1, C1, and D1 can be mapped and adjusted to points B2, C2, and D2 respectively to correct the image content of the monitoring image. If there is an overlap of pixels at the same coordinate position after the correction, the average value of multiple pixel values with the same coordinate position is taken as the pixel value at this coordinate position. After the above mapping and adjustment, a target image conforming to the direction of the reference image can be obtained.

[0060] S140. Perform type matching on the target image according to a preset type matching rule to obtain the corresponding road occupation monitoring type.

[0061] The type matching of the target image can be performed according to the type matching rule. Different occupation situations may exist in the channels of the target image. To distinguish different occupation situations, type matching can be performed to obtain the corresponding road occupation monitoring type. Then, each target image can obtain a corresponding road occupation monitoring type, and the road occupation monitoring type can reflect the specific occupation situation in the channel.

[0062] In a specific embodiment, step S140 includes sub-steps: partitioning the target image according to the image template in the type matching rule; obtaining the ratio value between the road occupation object and the channel cross-section in the target image based on the partitioning result; and obtaining the type matching the ratio value from the type matching rule as the corresponding road occupation monitoring type.

[0063] Since the target images have all been corrected, the target images can be partitioned based on the image template corresponding to the reference image to determine the respective orientations corresponding to the partitioned images. Further, the ratio value between the road occupation object and the channel cross-section in the target image is obtained based on the partitioning result. The ratio between the area of the pixels in the area with abnormal colors or the area of the pixels in the marked area in the target image and the pixel area of the channel cross-section in the target image can be obtained as the corresponding ratio value.

[0064] In the type matching rule, each type corresponds to a ratio interval. That is, the ratio intervals of each type can be matched with the proportional value corresponding to the target image, so as to obtain the type corresponding to the ratio interval containing the proportional value as the lane occupation monitoring type corresponding to the target image. For example, the types set in the type matching rule can include slight, moderate, severe, etc.

[0065] S150. Generate a corresponding alarm instruction according to the lane occupation monitoring type and send it to the alarm corresponding to the target image.

[0066] An alarm instruction corresponding to the lane occupation monitoring type of the target image can be generated and sent to the alarm on one side of the setting position of the image acquisition device corresponding to the target image. Each alarm can be respectively set on one side of an image acquisition device; after receiving the alarm instruction, the alarm can send out an alarm prompt message to remind the operators passing through the channel.

[0067] After performing the above steps, step S110 can be returned to execute to periodically collect the monitoring images of each channel, so as to realize the periodic monitoring of the channel occupation.

[0068] In the method for monitoring channel occupation based on intelligent image recognition disclosed in the above embodiments, the method includes: obtaining the collected monitoring images from the image acquisition device according to the preset acquisition interval time; judging each monitoring image according to the preset lane occupation judgment rule to obtain the judgment result of whether each monitoring image is suspected of lane occupation; if the judgment result is suspected of lane occupation, adjusting the monitoring image according to the preset image adjustment strategy to obtain the corresponding target image; performing type matching on the target image according to the preset type matching rule to obtain the corresponding lane occupation monitoring type; generating a corresponding alarm instruction according to the lane occupation monitoring type and sending it to the alarm corresponding to the target image. The above method for monitoring channel occupation can periodically collect monitoring images and judge whether there is suspected lane occupation. If there is suspected lane occupation, further adjust the monitoring image to obtain the target image and perform type matching, obtain the corresponding lane occupation monitoring type and generate an alarm instruction; it can realize the intelligent recognition of channel occupation and greatly improve the accuracy of channel occupation judgment.

[0069] An embodiment of the present invention further provides a device for monitoring channel occupation based on intelligent image recognition. The device for monitoring channel occupation based on intelligent image recognition can be configured in a management server, and the device for monitoring channel occupation based on intelligent image recognition is used to execute any one of the above embodiments of the method for monitoring channel occupation based on intelligent image recognition. Specifically, please refer to Figure 4 , Figure 4 which is a schematic block diagram of the device for monitoring channel occupation based on intelligent image recognition provided by an embodiment of the present invention.

[0070] As shown Figure 4 in FIG. Figure 4 , the channel occupancy monitoring device 100 based on intelligent image recognition includes a detection signal judgment unit 110, an image set acquisition unit 120, an intercepted image acquisition unit 130, an individual feature information acquisition unit 140, and a detection result acquisition unit 150.

[0071] The monitoring image acquisition unit 110 is configured to obtain the acquired monitoring images from the image acquisition device according to a preset acquisition interval time;

[0072] The judgment unit 120 is configured to judge each of the monitoring images according to a preset road occupation judgment rule to obtain a judgment result on whether each of the monitoring images is suspected of occupying the road;

[0073] The target image acquisition unit 130 is configured to, if the judgment result is suspected of occupying the road, perform image adjustment on the monitoring image according to a preset image adjustment strategy to obtain a corresponding target image;

[0074] The road occupation monitoring type acquisition unit 140 is configured to perform type matching on the target image according to a preset type matching rule to obtain a corresponding road occupation monitoring type;

[0075] The alarm instruction sending unit 150 is configured to generate a corresponding alarm instruction according to the road occupation monitoring type and send it to the alarm corresponding to the target image.

[0076] In the channel occupancy monitoring device based on intelligent image recognition provided in the embodiments of the present invention, the above-mentioned channel occupancy monitoring method based on intelligent image recognition is applied. The acquired monitoring images are obtained from the image acquisition device according to a preset acquisition interval time; each monitoring image is judged according to a preset road occupation judgment rule to obtain a judgment result on whether each monitoring image is suspected of occupying the road; if the judgment result is suspected of occupying the road, the monitoring image is subjected to image adjustment according to a preset image adjustment strategy to obtain a corresponding target image; the target image is subjected to type matching according to a preset type matching rule to obtain a corresponding road occupation monitoring type; a corresponding alarm instruction is generated according to the road occupation monitoring type and sent to the alarm corresponding to the target image. The above-mentioned channel occupancy monitoring method can periodically collect monitoring images and judge whether there is a suspected road occupation. If there is a suspected road occupation, the monitoring image is further subjected to image adjustment to obtain a target image and type matching is performed, the corresponding road occupation monitoring type is obtained and an alarm instruction is generated; it can realize intelligent recognition of channel occupancy and greatly improve the accuracy of channel occupancy judgment.

[0077] The above-mentioned channel occupancy monitoring device based on intelligent image recognition can be implemented in the form of a computer program, and the computer program can run on a computer device as shown Figure 5 in FIG. Figure 5 .

[0078] Please refer to Figure 5 , Figure 5 which is a schematic block diagram of a computer device provided by an embodiment of the present invention. The computer device may be a management server for executing a channel occupancy monitoring method based on intelligent image recognition to intelligently identify whether a channel is occupied.

[0079] Refer to Figure 5 , the computer device 500 includes a processor 502, a memory, and a communication interface 505 connected through a communication bus 501. Among them, the memory may include a storage medium 503 and an internal memory 504.

[0080] The storage medium 503 can store an operating system 5031 and a computer program 5032. When the computer program 5032 is executed, the processor 502 can be made to execute a channel occupancy monitoring method based on intelligent image recognition. Among them, the storage medium 503 can be a volatile storage medium or a non-volatile storage medium.

[0081] The processor 502 is used to provide computing and control capabilities to support the operation of the entire computer device 500.

[0082] The internal memory 504 provides an environment for the operation of the computer program 5032 in the storage medium 503. When the computer program 5032 is executed by the processor 502, the processor 502 can be made to execute a channel occupancy monitoring method based on intelligent image recognition.

[0083] The communication interface 505 is used for network communication, such as providing the transmission of data information, etc. Those skilled in the art can understand that Figure 5 the structure shown in

[0084] is only a block diagram of a part of the structure related to the solution of the present invention, and does not constitute a limitation on the computer device 500 to which the solution of the present invention is applied. The specific computer device 500 may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0085] Those skilled in the art can understand that Figure 5 the embodiment of the computer device shown in Figure 5This is consistent with the illustrated embodiments and will not be elaborated herein.

[0086] It should be understood that in the embodiments of the present invention, the processor 502 may be a central processing unit (CPU), and the processor 502 may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. Among them, the general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc.

[0087] In another embodiment of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium may be a volatile or non-volatile computer-readable storage medium. The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps included in the above-mentioned channel occupancy monitoring method based on intelligent image recognition are implemented.

[0088] Those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working processes of the above-described devices, apparatuses, and units can refer to the corresponding processes in the foregoing method embodiments and will not be elaborated herein. Those of ordinary skill in the art can realize that the units and algorithm steps of the examples described in conjunction with the embodiments disclosed herein can be implemented by electronic hardware, computer software, or a combination of the two. To clearly illustrate the interchangeability of hardware and software, the components and steps of each example have been generally described according to their functions in the above description. Whether these functions are executed in a hardware or software manner depends on the specific application and design constraints of the technical solution. Professional technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered to exceed the scope of the present invention.

[0089] In several embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods, or units with the same function can be aggregated into one unit. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Additionally, the displayed or discussed couplings or direct couplings or communication connections to each other can be indirect couplings or communication connections through some interfaces, devices, or units, or can be electrical, mechanical, or other forms of connection.

[0090] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiments of the present invention.

[0091] In addition, the functional units in each embodiment of the present invention can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0092] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a computer-readable storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The aforementioned computer-readable storage medium includes: USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), magnetic disks, or optical discs, etc., which can store program codes.

[0093] As described above, the above are only specific implementation manners of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present invention can easily think of various equivalent modifications or substitutions, and these modifications or substitutions should be covered within the protection scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.

Claims

1. A channel occupancy monitoring method based on intelligent image recognition, characterized in that The method is applied to a management server which establishes a network connection with image acquisition devices and alarms set at each channel to achieve data information transmission. The method includes: Obtaining the acquired monitoring images from the image acquisition devices according to a preset acquisition interval time; Judging each of the monitoring images according to a preset road occupation judgment rule to obtain a judgment result on whether each of the monitoring images is suspected of road occupation; If the judgment result is suspected of road occupation, adjusting the monitoring images according to a preset image adjustment strategy to obtain corresponding target images; Performing type matching on the target images according to a preset type matching rule to obtain corresponding road occupation monitoring types; Generating a corresponding alarm instruction according to the road occupation monitoring type and sending it to the alarm corresponding to the target image.

2. The channel occupancy monitoring method based on intelligent image recognition according to claim 1, characterized in that The judging each of the monitoring images according to a preset road occupation judgment rule to obtain a judgment result on whether each of the monitoring images is suspected of road occupation includes: Judging whether there is an abnormal color in each of the monitoring images according to the color parameter information in the road occupation judgment rule to obtain a corresponding abnormal color judgment result; Extracting a corresponding channel boundary contour from each of the monitoring images according to the boundary extraction parameter in the road occupation judgment rule; Judging whether there is an abnormality in each of the channel boundary contours according to the boundary abnormality detection parameter in the road occupation judgment rule to obtain a corresponding boundary contour judgment result; If the abnormal color judgment result is that there is an abnormality or the boundary contour judgment result is that there is an abnormality, obtaining a judgment result of being suspected of road occupation; If the abnormal color judgment result is that there is no abnormality and the boundary contour judgment result is that there is no abnormality, obtaining a judgment result of not being suspected of road occupation.

3. The channel occupancy monitoring method based on intelligent image recognition according to claim 2, wherein, The judging whether there is an abnormal color in each of the monitoring images according to the color parameter information in the road occupation judgment rule to obtain a corresponding abnormal color judgment result includes: Obtaining a color interval corresponding to the monitoring image from the color parameter information; Obtaining the pixel points not in the color interval in the monitoring image as abnormal pixel points; Obtaining the connected domain corresponding to the abnormal pixel points in the monitoring image; Judging whether the area of each of the connected domains is greater than the area threshold in the color parameter information to determine whether there is an abnormal color in the monitoring image.

4. The channel occupancy monitoring method based on intelligent image recognition according to claim 2, wherein The extracting a corresponding channel boundary contour from each of the monitoring images according to the boundary extraction parameter in the road occupation judgment rule includes: Calculating the contrast coefficient of each pixel point in the monitoring image; Obtaining partial pixel points corresponding to the extraction ratio from the monitoring image as alternative pixel points according to the extraction ratio in the boundary extraction parameter and the contrast coefficient; Binarizing the alternative pixel points to obtain a corresponding pixel contour; Selecting a pixel contour matching the boundary reference direction from the pixel contour according to the boundary reference direction in the boundary extraction parameter as the corresponding channel boundary contour.

5. The channel occupancy monitoring method based on intelligent image recognition according to claim 4, wherein Judging whether there is an abnormality in each of the channel boundary contours according to the boundary abnormality detection parameter in the lane occupation judging rule, and obtaining corresponding boundary contour judging results, including: Judging whether the extension directions of the channel boundary contours are the same; If the extension directions of two of the channel boundary contours are the same, obtaining a deviation coefficient between the two channel boundary contours with the same extension direction according to the basic parameter in the boundary abnormality detection parameter; Judging whether the deviation coefficient of the two channel boundary contours with the same extension direction is within the deviation interval in the boundary abnormality detection parameter to determine whether there is an abnormality in the channel boundary contour.

6. The channel occupancy monitoring method based on intelligent image recognition according to any one of claims 1-5, characterized in that, Adjusting the monitoring image according to a preset image adjustment strategy to obtain a corresponding target image, including: Obtaining an adjustment mapping model corresponding to the monitoring image in the image adjustment strategy; Performing mapping adjustment on the coordinate positions of each pixel point in the monitoring image according to the coordinate mapping relationship in the adjustment mapping model to obtain a corresponding target image.

7. The channel occupancy monitoring method based on intelligent image recognition according to any one of claims 1-5, characterized in that, Performing type matching on the target image according to a preset type matching rule to obtain a corresponding lane occupation monitoring type, including: Partitioning the target image according to the image template in the type matching rule; Obtaining a ratio value between the lane occupation object and the channel cross-section in the target image based on the partitioning result; Obtaining a type matching the ratio value from the type matching rule as the corresponding lane occupation monitoring type.

8. A channel occupancy monitoring device based on intelligent image recognition, characterized in that, The device is configured in a management server, and the management server establishes a network connection with an image acquisition device and an alarm installed at each channel to realize data information transmission. The device is used to execute the lane occupation monitoring method based on intelligent image recognition according to any one of claims 1-7. The device includes: A monitoring image acquisition unit, configured to acquire the acquired monitoring image from the image acquisition device according to a preset acquisition interval time; A judging unit, configured to judge each of the monitoring images according to a preset lane occupation judging rule to obtain a judging result on whether each of the monitoring images is suspected of lane occupation; A target image acquisition unit, configured to, if the judging result is suspected of lane occupation, adjust the monitoring image according to a preset image adjustment strategy to obtain a corresponding target image; A lane occupation monitoring type acquisition unit, configured to perform type matching on the target image according to a preset type matching rule to obtain a corresponding lane occupation monitoring type; An alarm instruction sending unit, configured to generate a corresponding alarm instruction according to the lane occupation monitoring type and send it to the alarm corresponding to the target image.

9. A computer device, characterized in that, The device includes a processor, a communication interface, a memory, and a communication bus. Among them, the processor, the communication interface, and the memory complete communication with each other through the communication bus; The memory is used to store a computer program; The processor, when executing the program stored on the memory, implements the steps of the lane occupation monitoring method based on intelligent image recognition according to any one of claims 1-7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the steps of the lane occupation monitoring method based on intelligent image recognition according to any one of claims 1-7.

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