Pavement visibility processing method and system based on image analysis, and storage medium

Abstract

The invention discloses a road visibility processing method and system based on image analysis and a storage medium, and relates to the technical field of road image monitoring, and the method comprises the steps: obtaining the position information of at least two imaging collection point positions on a monitored road section according to the design information of traffic facilities, and the road condition image information of the monitored road section is acquired in real time by using the imaging acquisition point. According to the invention, the movement track of the monitored target on the road is acquired in real time by using the imaging acquisition point, the trend of the monitored target is dynamically captured in real time according to the movement track feedback of the monitored target, and then whether the monitored target has an accident can be deduced. Meanwhile, the accident level of the monitored target can be deduced according to the real-time road condition image. Therefore, the defect that the accident condition cannot be known in time due to low road traffic visibility in the prior art is effectively overcome.

Description

Technical Field

[0001] The present invention relates to the technical field of road image monitoring, and in particular to a road visibility processing method, system and storage medium based on image analysis. Background Art

[0002] Low-visibility meteorological disasters are disasters of road traffic facilities. Their main cause is heavy fog or fog, which will seriously affect vehicles traveling on the road and pedestrians passing by. They greatly affect the driver's line of sight in road traffic, increase the risk of traffic accidents, and pose a serious threat to road safety. Not only will it make it difficult for the driver to see the road and traffic signs ahead, it may also increase the braking distance of the vehicle, making it difficult to respond in time.

[0003] On the one hand, low visibility weather greatly increases the rate of road traffic accidents. On the other hand, low visibility makes it impossible to understand the accident situation in time after a road traffic accident occurs, which in turn affects rescue. Summary of the invention

[0004] The purpose of the present invention is to solve the shortcomings of the prior art that the accident situation cannot be understood in time due to low road traffic visibility, and to propose a road visibility processing method, system and storage medium based on image analysis.

[0005] In order to achieve the above object, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a road visibility processing method based on image analysis, comprising:

[0007] According to the traffic facility design information, the location information of at least two imaging collection points on the monitored road section is obtained, and the road condition image information of the monitored road section is collected in real time using the imaging collection points;

[0008] The road condition image information of the monitored section is collected in real time according to the imaging collection points, the visibility level of the monitored section is obtained, and the accident occurrence signal is determined in combination with the activity trajectory of the monitored target;

[0009] According to the accident signal and the imaging acquisition points, the road condition image information of the monitored section is collected in real time, and the accident section interval is pre-determined in combination with the position information of the imaging acquisition points on the monitored section;

[0010] According to the predetermined accident section, and in combination with the traffic facility design information, the location information of at least one accident imaging collection point on the accident section is determined, and the accident imaging collection point is used to collect the road condition image of the accident section in real time;

[0011] The accident status of the monitored target is determined based on the road condition image of the accident section and the activity trajectory of the monitored target.

[0012] In some optional solutions, the method for obtaining the visibility level of the monitored road section includes:

[0013] The imaging acquisition points collect the road condition image information of the monitored road section in real time, perform image recognition analysis, and obtain the road condition image feature distribution information of the monitored road section;

[0014] According to the road condition image feature distribution information of the monitored road section and in combination with the position information of the imaging acquisition points, the road condition image information of the monitored road section is divided into image regions to obtain the road condition image feature distribution information of at least two sub-regions in the target road section;

[0015] According to the traffic image feature distribution information of the target road section, the traffic image feature distribution information of at least two sub-areas in the target road section is compared to obtain the image feature difference information of each sub-area in the target road section;

[0016] The visibility level of the monitored road section is determined based on the image feature difference information of each sub-area in the target road section.

[0017] In some optional solutions, the traffic image feature distribution information of the monitored road section includes:

[0018] Image grayscale value.

[0019] In some optional solutions, the method for determining the accident occurrence signal includes:

[0020] According to the design information of traffic facilities and the location information of imaging acquisition points, combined with the activity trajectory of the monitored target, the pre-activity dynamic information of the monitored target is determined;

[0021] According to the pre-activity dynamic information of the monitored target, the location information of the imaging acquisition point is determined, and the imaging acquisition point is used to collect the current road condition image information in real time;

[0022] According to the pre-activity dynamic information of the monitored target, the road condition image features of the monitored section are identified and analyzed to obtain the abnormal signal of the monitored section;

[0023] The accident occurrence signal is determined based on the abnormal signal of the monitored road section and the pre-activity dynamic information of the monitored target.

[0024] In some optional solutions, the pre-activity dynamic information of the monitored target includes:

[0025] A combination of one or more of the movement speed and movement direction of the monitored target.

[0026] In some optional solutions, the method for determining the accident status of the monitored target includes:

[0027] According to the position information of the imaging acquisition point, a point is selected in the road condition image of the accident section as the first reference point;

[0028] Constructing a position division coordinate system in the traffic image of the target road section with the first reference point; obtaining the position coordinates of each area in the traffic image of the target road section according to the position division coordinate system;

[0029] Extracting image grayscale features from the road condition image of the target road section to determine image feature information of reference objects in the target road section;

[0030] According to the position division coordinate system and in combination with the image feature information of the reference object in the target road section, the road condition image of the target road section is divided into multiple regions along the longitudinal coordinate direction or the transverse coordinate direction of the position division coordinate system, at least two section distribution maps are obtained in the road condition image of the target road section, and the gray value information in each section distribution map is determined respectively in combination with the image feature information of the reference object in the target road section;

[0031] According to the gray value information in the distribution map of each section and combined with the activity trajectory of the monitored target, the accident status of the monitored target is determined.

[0032] In a second aspect, the present invention provides a road visibility processing system based on image analysis, which adopts any one of the road visibility processing methods based on image analysis described in the first aspect.

[0033] In some optional solutions, the processing system further includes:

[0034] An image acquisition module, which is arranged at an imaging point and is used to acquire road condition image information in real time;

[0035] An image processing module, wherein the image processing module is used to perform image processing on road condition image information;

[0036] An information identification module, which is used to determine whether there is an abnormality in the monitored road section;

[0037] The early warning feedback module is used to feedback the accident status.

[0038] In a third aspect, the present invention provides a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, a road visibility processing method based on image analysis as described in any one of the first aspects is implemented.

[0039] The beneficial effects of the present invention are:

[0040] The present invention uses imaging acquisition points to collect the activity trajectory of the monitored target on the road in real time, and dynamically captures the direction of the monitored target in real time based on the activity trajectory feedback of the monitored target, and then can deduce whether the monitored target has an accident. At the same time, the accident level of the monitored target can also be derived based on the real-time road condition image. That is, it effectively solves the disadvantage of the prior art that the accident situation cannot be understood in time due to low road traffic visibility. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic diagram of the overall process of a road visibility processing method based on image analysis provided in an embodiment of the present invention;

[0042] Figure 2 A partial flow chart of a road visibility processing method based on image analysis provided in an embodiment of the present invention;

[0043] Figure 3 A schematic diagram of a position division coordinate system division of a road visibility processing method based on image analysis provided in an embodiment of the present invention;

[0044] Figure 4 A schematic diagram of contour simulation reference line division for a road visibility processing method based on image analysis provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] It should be noted that all directional indications in the embodiments of the present invention (such as up, down, left, right, front, back, etc.) are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0047] In the present invention, unless otherwise clearly specified and limited, the terms "connection", "fixation", etc. should be understood in a broad sense. For example, "fixation" can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection or an indirect connection through an intermediate medium, it can be the internal connection of two elements or the interaction relationship between two elements, unless otherwise clearly defined. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0048] In addition, if there are descriptions involving "first", "second", etc. in the embodiments of the present invention, the descriptions of "first", "second", etc. are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, the meaning of "and / or" appearing in the full text includes three parallel schemes. Taking "A and / or B" as an example, it includes scheme A, or scheme B, or a scheme that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, but it must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be deemed that such a combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0049] Example

[0050] Reference Figures 1 to 4 In order to solve the shortcomings of the prior art that the accident situation cannot be timely understood due to low road traffic visibility, the present invention provides, in this embodiment, a road visibility processing method based on image analysis in the first aspect, which uses imaging acquisition points to collect the activity trajectory of the monitored target on the road in real time, and dynamically captures the direction of the monitored target in real time according to the activity trajectory feedback of the monitored target, and then can deduce whether the monitored target has an accident. At the same time, the accident level of the monitored target can also be derived based on the real-time road condition image. That is, it effectively solves the shortcomings of the prior art that the accident situation cannot be timely understood due to low road traffic visibility.

[0051] Specifically, the processing method includes: obtaining the position information of at least two imaging acquisition points on the monitored road section according to the traffic facility design information, and using the imaging acquisition points to collect the road condition image information of the monitored road section in real time; that is, after the accident occurs or when the early warning drill is carried out, the area of ​​the monitored road section is determined in advance, and then at least two imaging acquisition points and their positions are determined according to the traffic route planning of the monitored road section area, and the road condition image information is collected through the imaging acquisition points. So as to provide a reference basis for the further determination of the accident section in the future. Then, the road condition image information of the monitored road section is collected in real time according to the imaging acquisition points, and the visibility level of the monitored road section is obtained, that is, whether there is heavy fog or other low visibility conditions at the current position of the monitored road section area is determined, and at the same time, the accident occurrence signal is determined in combination with the activity trajectory of the monitored target, that is, according to the driving trajectory of the monitored target and the visibility conditions in the monitored road section area, it can be determined whether the monitored target has an accident in this road section, and then the road condition conditions in the monitored road section area can be further screened. That is, if an accident occurs in this monitored section, the road condition image information of the monitored section can be collected in real time according to the accident occurrence signal and the imaging acquisition point, and the accident section interval can be pre-determined in combination with the position information of the imaging acquisition point on the monitored section, that is, it can be determined that an accident has occurred in the monitored section. Then, according to the pre-determined accident section interval, and in combination with the traffic facility design information, the position information of at least one accident imaging acquisition point on the accident section interval can be determined, and the accident imaging acquisition point can be used to collect the road condition image of the accident section interval in real time; according to the road condition image of the accident section interval, and in combination with the activity trajectory of the monitored target, the accident status of the monitored target can be determined. In this embodiment, the position information of the monitored section area can be determined in a large range according to the imaging point in the traffic facility design information, and then the accident location can be searched in the monitored section area according to the activity trajectory of the monitored target, that is, the location of the accident area of ​​the monitored target can be judged according to whether the monitored target appears in the monitored section area. Then, the road condition is framed according to the imaging acquisition points near the position to determine the status of the accident. That is, by using imaging acquisition points to collect the activity trajectory of the monitored target on the road in real time, the direction of the monitored target can be captured in real time based on the feedback of the activity trajectory of the monitored target, and then it can be deduced whether the monitored target has an accident. At the same time, the accident level of the monitored target can also be deduced based on the real-time road condition image. This effectively solves the shortcomings of the existing technology that the accident situation cannot be understood in time due to low road traffic visibility.

[0052] In this embodiment, in order to facilitate understanding of how to analyze the visibility level of the monitored road section, the following description is given. Specifically, the method for obtaining the visibility level of the monitored road section includes:

[0053] The imaging acquisition points collect the road condition image information of the monitored section in real time, perform image recognition analysis, and obtain the road condition image feature distribution information of the monitored section; that is, the grayscale value of a certain fixed feature in the road condition image of the monitored section can be extracted to determine the road condition image feature distribution information in the road condition image of the monitored section; then, based on the road condition image feature distribution information of the monitored section and combined with the position information of the imaging acquisition points, the road condition image information of the monitored section is divided into image areas to obtain the road condition image feature distribution information of at least two sub-areas in the target section; that is, when it is necessary to determine the visibility level, the grayscale value of a certain fixed feature in the road condition image of the monitored section can be obtained in advance, and then based on the pre-acquired The grayscale value of a certain fixed feature in the road condition image of the monitored section can be used to divide the image area of ​​the road condition image collected in real time according to the actual distance, and the road condition division image of the target section (sub-region road condition image feature distribution information) is determined. Then, according to the road condition image feature distribution information of the target section, the road condition image feature distribution information of at least two sub-regions in the target section is compared to obtain the image feature difference information of each sub-region in the target section; that is, the sub-region road condition image feature distribution information is respectively compared with the grayscale value of a certain fixed feature in the road condition image of the monitored section obtained in advance to determine the grayscale value difference between the two, and then the visibility level of the monitored section is determined according to the image feature difference information of each sub-region in the target section.

[0054] In this embodiment, in order to facilitate understanding of how to determine the accident occurrence signal, the following description is given here. Specifically, the method for determining the accident occurrence signal includes:

[0055] According to the design information of traffic facilities and the position information of imaging acquisition points, combined with the activity trajectory of the monitored target, the pre-activity dynamic information of the monitored target is determined; that is, according to the driving trajectory and driving dynamic data (such as movement speed and movement direction) of the monitored target, the pre-passing point of the monitored target in the monitored section can be determined. Then, according to the pre-activity dynamic information of the monitored target, the position information of the imaging acquisition points is determined, and the current road condition image information is collected in real time using the imaging acquisition points; according to the pre-passing points of the monitored target in the monitored section, when the monitored target does not pass the next pre-passing point, the position of the imaging acquisition points of the section where the accident may occur is determined, and the imaging acquisition points of the target section are used to collect road condition images. That is, according to the pre-activity dynamic information of the monitored target, the road condition image features of the monitored section are identified and analyzed to obtain the abnormal signal of the monitored section; then, according to the abnormal signal of the monitored section and the pre-activity dynamic information of the monitored target, the accident occurrence signal is determined. In this embodiment, when the monitored target has not passed the next expected passing point or the necessary passing point for a long time, the road section monitoring of the monitored target can be started, that is, the search range can be gradually narrowed from the starting position to the end position, the previous passing point to the next expected passing point of the monitored target, and finally the location of the section where the accident occurred of the monitored target is determined, and then according to the traffic facility design information, the nearest imaging acquisition point in the section where the accident occurred of the monitored target is retrieved, and real-time road condition image acquisition is performed, and then the location and area where the accident occurred of the monitored target is determined. That is, the pre-activity dynamic information of the monitored target includes: one or more combinations of the movement speed and movement direction of the monitored target.

[0056] In this embodiment, in order to facilitate understanding of how to determine and analyze the accident status of the monitored target, the following description is given. Specifically, the method for determining the accident status of the monitored target includes:

[0057] A point can be selected as the first reference point in the road condition image of the accident section according to the position information of the imaging acquisition point in advance; then a position division coordinate system is constructed in the road condition image of the target section with the first reference point; according to the position division coordinate system, the position coordinates of each area in the road condition image of the target section are obtained; then, the image gray value feature extraction is performed on the road condition image of the target section to determine the image feature information of the reference object in the target section; that is, the road condition image feature extraction is performed on multiple imaging acquisition points in advance, and a point is selected in the road condition image of multiple imaging acquisition points. The gray value of the reference object is fixed, and then the road condition image of the target road section is divided into multiple regions along the longitudinal coordinate direction or the transverse coordinate direction of the position division coordinate system according to the position division coordinate system and the reference object image feature information in the target road section, and at least two section distribution maps are obtained in the road condition image of the target road section, and the gray value information in each section distribution map is determined respectively in combination with the reference object image feature information in the target road section; then the accident condition of the monitored target is determined according to the gray value information in each section distribution map and the activity trajectory of the monitored target. That is, the reference object is selected in advance in the road condition image of each imaging acquisition point of the monitored road section, and then the gray value feature of the reference object is image analyzed. When the monitored target appears in the target road section and an accident occurs, the gray value feature of the pre-set reference object is compared to determine the occurrence of the monitored target in the area, whether the parts of the monitored target are scattered or disintegrated, and then the accident condition characteristics of the monitored target are judged. It should be noted that the reference object can be a road surface, a flower bed, a guardrail, etc.

[0058] In this embodiment, in order to more quickly and accurately determine the location of the monitored target in the traffic road of the monitored section, the processing method also includes: obtaining the location information of all imaging acquisition points on the monitored section according to the design layout information of the traffic facilities; that is, the location information such as the geographical location distance relationship between all imaging acquisition points in the monitored section can be determined according to the design route of the traffic facilities, so as to provide an effective judgment basis for the subsequent determination of the location of the monitored target. That is, according to the location information of all imaging acquisition points on the monitored section, the imaging area of ​​the imaging acquisition points is integrated, and at least two intervals are obtained on the monitored section; then, according to the at least two intervals obtained by division, the regional feature information of the monitored section collected in real time by all imaging acquisition points is identified, and the real-time road condition feature information of each interval is obtained respectively; that is, the image gray value feature is extracted for each divided interval, and then according to the abnormal road condition warning model, and combined with the real-time road condition feature information of each interval, the monitored section is extracted to obtain the abnormal interval of the monitored section, so as to determine the accident location in the monitored section. Specifically, in order to facilitate understanding of the above description, how to determine the location of the accident in the monitored road section, the following example is given:

[0059] Assume that there are several imaging acquisition points on the monitored road section, and the imaging acquisition points are distributed in sequence. The coordinate values ​​of the imaging acquisition points are A1(x1, y1, z1), A2(x2, y2, z2), A3(x3, y3, z3), ..., A n (x n ,y n , z n ), since each imaging acquisition point can be considered to be on the same distribution surface according to the design line, the z-axis coordinate value of each imaging acquisition point can be ignored. Therefore, the coordinate values ​​of several imaging acquisition points are A1(x1, y1), A2(x2, y2), A3(x3, y3), ..., A n (x n ,y n ). From this we can see that if n There is A a To A b The interval section contains the location where the accident occurred in the monitored section. After combining the above-mentioned image gray value feature extraction of the accident status of the monitored target and the coordinates of each imaging acquisition point, the location can be quickly marked and positioned directly through the coordinate system.

[0060] In this embodiment, since the actual distance range of the imaging acquisition point is wide, in order to realize the rapid positioning of the position distance of the monitored target in the road condition image of the imaging acquisition point, the processing method also includes: obtaining the nearest imaging acquisition point in the accident-occurring section of the monitored target, and using the imaging acquisition point to perform real-time road condition image acquisition to obtain the accident-occurring area image of the monitored target, and selecting a point in the accident-occurring area image of the monitored target as the first reference point; and then constructing a position division coordinate system in the road condition image of the accident-occurring area image of the monitored target with the first reference point; and obtaining the monitored target according to the position division coordinate system. The position coordinates of each area in the accident area image; extract the image grayscale value features of the accident area image of the monitored target, and match and determine the reference image feature information corresponding to the accident area image of the monitored target; according to the position division coordinate system, and combined with the reference image feature information in the monitored road section, the accident area image of the monitored target can be divided into multiple areas along the longitudinal coordinate direction or the transverse coordinate direction of the position division coordinate system, and at least two section distribution maps are obtained in the accident area image of the monitored target, and the grayscale value information in each section distribution map is determined respectively in combination with the reference image feature information in the monitored road section. That is, according to the reference grayscale value corresponding to the monitored target in the accident area image of the monitored target, the actual distance position of the monitored target can be determined.

[0061] Reference Figure 3 and Figure 4 In order to more accurately determine the actual distance position of the monitored target, the first section dividing line and the second section dividing line are set in parallel along the longitudinal coordinate direction of the position dividing coordinate system, and the accident occurrence area image of the monitored target is divided into three sections. The distance between the first reference point and the first section dividing line is L1 meters, the distance between the first reference point and the second section dividing line is L2 meters, and the second section dividing line is L3 meters. Assuming that the contour outer line M of the monitored target is between the first section dividing line and the second section dividing line, a contour simulation reference line N can be made along the contour outer line M of the monitored target, and the contour simulation reference line N is used as the position line of the monitored target entering between the first section dividing line and the second section dividing line, wherein the contour simulation reference line N is parallel to the first section dividing line and the second section dividing line. At this time, the distance between the contour simulation reference line N and the first reference point is the visibility distance L. n At this time, the distance between the contour simulation reference line N and the first reference point can be calculated by the following method:

[0062] Assume that the coordinate value of the first reference point is A(x1, y1) and the imaging area of ​​the imaging acquisition point is divided equally. Therefore, it can be known that the point B(x1-a, y1) located on one side of the first reference point, that is, the first reference point divides the image of the accident area of ​​the monitored target equally on the vertical coordinate. The coordinate system is divided according to the size and position of the image of the accident area of ​​the monitored target, and the contour simulation reference line N is parallel to the first section dividing line and the second section dividing line. It can be known that the coordinates of the contour simulation reference line N at both ends of the image of the accident area of ​​the monitored target are C(x1-a, y1+n). At this time, according to

[0063]

[0064] in, |AB| is the straight-line distance from point A to point B; |AC| is the straight-line distance from point A to point C; |BC| is the straight-line distance from point B to point C; that is, in this embodiment, at this time:

[0065]

[0066] On the other hand, since the imaging area of ​​the accident occurrence area image of the monitored target is a rectangle, it can be known from the calculation method of the rectangle:

[0067]

[0068] At this time, since the imaging area size of the accident area image of the monitored target is known, and the first reference point divides the imaging area of ​​the imaging acquisition point into two halves, the value of a is known. Therefore, the value of n can be known by combining Formula 1 and Formula 2, that is, the distance between the fog simulation reference line N and the first reference point can be known, thereby judging the visibility distance.

[0069] In a preferred embodiment, in order to reduce the image processing pressure on the road condition image information of the monitored road section, the processing method also includes a method for determining the regional feature information of the monitored road section. This method uses fixed reference objects (such as road guardrails, plants on both sides or in the middle of the road, etc.) or moving reference objects (such as vehicles on the road) in the traffic road facilities in the road condition image information of the monitored road section to cause image feature changes in the road condition image information of the monitored road section. Perform regional analysis to reduce unnecessary image feature recognition, thereby alleviating the image processing pressure of the road condition image information of the monitored road section. Specifically, it also includes: the road condition image information of the monitored road section can be collected in real time by the imaging acquisition point for image recognition analysis to obtain the gray value distribution information of the road condition image of the monitored road section; then, according to the gray value distribution information of the road condition image of the monitored road section and in combination with the position information of the imaging acquisition point, the road condition image information of the monitored road section is divided into image regions to obtain the gray value distribution information of the road condition image of at least two sub-regions in the monitored road section; that is, according to the position information of the imaging acquisition point and the gray value distribution information of the road condition image of the monitored road section, the road condition image information of the monitored road section can be divided into regions. That is, according to the coordinate system of the imaging acquisition point and the gray value of the image feature, the fixed reference objects or mobile reference objects in the same region of the road condition image information of the monitored road section can be divided into regions to determine the regional positions of each different reference object in the road condition image information of the monitored road section. That is, according to the grayscale value distribution information of the road condition image of the monitored section, the grayscale value distribution information of the road condition image of at least two sub-areas in the monitored section is compared, and the grayscale difference information of the images of each sub-area in the monitored section is obtained.

[0070] In the second aspect, the present invention provides a road visibility processing system based on image analysis, which adopts a road visibility processing method based on image analysis described in any one of the first aspects. In this embodiment, the processing system also includes: an image acquisition module, which is set at the acquisition imaging point, and the image acquisition module is used to collect road condition image information in real time; an image processing module, which is used to perform image processing on the road condition image information; an information recognition module, which is used to determine whether there is an abnormality in the monitored road section; and an early warning feedback module, which is used to feedback the accident situation. The processing system uses the imaging acquisition point to collect the activity trajectory of the monitored target on the road in real time, and dynamically captures the direction of the monitored target in real time according to the activity trajectory feedback of the monitored target, and then can deduce whether the monitored target has an accident. At the same time, the accident level of the monitored target can also be derived according to the real-time road condition image. That is, it effectively solves the shortcomings of the prior art that the accident situation cannot be understood in time due to low road traffic visibility.

[0071] In some embodiments, the processing system can communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and can be interconnected with 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"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0072] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0073] The third aspect of the present invention provides a computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, a road visibility processing method based on image analysis as described in any one of the first aspects is implemented. The computer-readable medium in this embodiment can be written in one or more programming languages ​​or a combination thereof to write computer program codes for performing the operations of some embodiments of the present disclosure, wherein the programming languages ​​include object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as an independent software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer can be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or can be connected to an external computer (for example, using an Internet service provider to connect through the Internet).

[0074] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0075] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer readable medium, and the computer program includes a program code for executing the method shown in the flowchart.

[0076] The fourth aspect of the present invention provides an electronic device, comprising: one or more processors; a storage device having one or more programs stored thereon; when the one or more programs are executed by the one or more processors, the one or more processors implement a road visibility processing method based on image analysis as described in the first aspect. The above-mentioned computer-readable medium may be included in the above-mentioned electronic device; or it may exist separately, that is, not assembled in the electronic device. The above-mentioned computer-readable medium carries one or more programs, and when the above-mentioned one or more programs are executed by the electronic device, the electronic device can implement a road visibility processing method based on image analysis as described in the first aspect.

[0077] A fifth aspect of the present invention provides a computer program product, comprising a computer program, which, when executed by a processor, implements a road visibility processing method based on image analysis as described in the first aspect.

[0078] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A road visibility processing method based on image analysis, characterized in that: include: According to the traffic facility design information, the location information of at least two imaging collection points on the monitored road section is obtained, and the road condition image information of the monitored road section is collected in real time using the imaging collection points; The road condition image information of the monitored section is collected in real time according to the imaging collection points, the visibility level of the monitored section is obtained, and the accident occurrence signal is determined in combination with the activity trajectory of the monitored target; According to the accident signal and the imaging acquisition points, the road condition image information of the monitored section is collected in real time, and the accident section interval is pre-determined in combination with the position information of the imaging acquisition points on the monitored section; According to the predetermined accident section, and in combination with the traffic facility design information, the location information of at least one accident imaging collection point on the accident section is determined, and the accident imaging collection point is used to collect the road condition image of the accident section in real time; The accident status of the monitored target is determined based on the road condition image of the accident section and the activity trajectory of the monitored target.

2. The method for processing road visibility based on image analysis according to claim 1, characterized in that: The method for obtaining the visibility level of the monitored road section includes: The imaging acquisition points collect the road condition image information of the monitored road section in real time, perform image recognition analysis, and obtain the road condition image feature distribution information of the monitored road section; According to the road condition image feature distribution information of the monitored road section and in combination with the position information of the imaging acquisition points, the road condition image information of the monitored road section is divided into image regions to obtain the road condition image feature distribution information of at least two sub-regions in the target road section; According to the traffic image feature distribution information of the target road section, the traffic image feature distribution information of at least two sub-areas in the target road section is compared to obtain the image feature difference information of each sub-area in the target road section; The visibility level of the monitored road section is determined based on the image feature difference information of each sub-area in the target road section.

3. The method for processing road visibility based on image analysis according to claim 2, characterized in that: The traffic image feature distribution information of the monitored road section includes: Image grayscale value.

4. The method for processing road visibility based on image analysis according to claim 3, characterized in that: The method for determining an accident occurrence signal comprises: According to the design information of traffic facilities and the location information of imaging acquisition points, combined with the activity trajectory of the monitored target, the pre-activity dynamic information of the monitored target is determined; According to the pre-activity dynamic information of the monitored target, the location information of the imaging acquisition point is determined, and the imaging acquisition point is used to collect the current road condition image information in real time; According to the pre-activity dynamic information of the monitored target, the road condition image features of the monitored section are identified and analyzed to obtain the abnormal signal of the monitored section; The accident occurrence signal is determined based on the abnormal signal of the monitored road section and the pre-activity dynamic information of the monitored target.

5. The method for processing road visibility based on image analysis according to claim 4, characterized in that: The pre-activity dynamic information of the monitored target includes: A combination of one or more of the movement speed and movement direction of the monitored target.

6. The method for processing road visibility based on image analysis according to claim 5, characterized in that: The method for determining the accident status of the monitored target comprises: According to the position information of the imaging acquisition point, a point is selected in the road condition image of the accident section as the first reference point; Constructing a position division coordinate system in the traffic image of the target road section with the first reference point; obtaining the position coordinates of each area in the traffic image of the target road section according to the position division coordinate system; Extracting image grayscale features from the road condition image of the target road section to determine image feature information of reference objects in the target road section; According to the position division coordinate system and in combination with the image feature information of the reference object in the target road section, the road condition image of the target road section is divided into multiple regions along the longitudinal coordinate direction or the transverse coordinate direction of the position division coordinate system, at least two section distribution maps are obtained in the road condition image of the target road section, and the gray value information in each section distribution map is determined respectively in combination with the image feature information of the reference object in the target road section; According to the gray value information in the distribution map of each section and combined with the activity trajectory of the monitored target, the accident status of the monitored target is determined.

7. A road visibility processing system based on image analysis, characterized in that: A road visibility processing method based on image analysis as described in any one of claims 1 to 6 is adopted.

8. The road visibility processing system based on image analysis according to claim 7, characterized in that: The processing system further comprises: An image acquisition module, which is arranged at an imaging point and is used to acquire road condition image information in real time; An image processing module, wherein the image processing module is used to perform image processing on road condition image information; An information identification module, which is used to determine whether there is an abnormality in the monitored road section; The early warning feedback module is used to feedback the accident status.

9. A computer readable medium having a computer program stored thereon, characterized in that: in, When the program is executed by a processor, a road visibility processing method based on image analysis as described in any one of claims 1 to 6 is implemented.

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