Object detection method and system for highway

Abstract

The present invention provides an object detection method and system for a highway, the method comprising: collecting real-time physical parameters of a structure located on the highway in real time through a first preset acquisition device, and collecting real-scene images corresponding to the structure in real time through a second preset acquisition device; fusing the real-time physical parameters and the real-scene images to generate a corresponding monitoring data set in real time, and inputting the monitoring data set into a preset data processing model; performing corresponding quantitative processing on the monitoring data set through the preset data processing model to generate a corresponding target detection value in real time, and judging in real time whether the target detection value is greater than a preset detection threshold; if it is judged in real time that the target detection value is greater than the preset detection threshold, generating corresponding alarm information in real time, and generating corresponding maintenance information in real time. The present invention can accurately complete object detection, and correspondingly improves work efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to an object detection method and system for highways. Background Art

[0002] With the advancement of science and technology and the rapid development of the times, in order to improve people’s travel efficiency and transportation efficiency, numerous highways have been built to provide convenience for people.

[0003] Among them, with the continuous expansion of highway construction and the extension of service life, it is necessary to regularly maintain the highway. Specifically, since existing highways use more bridges, tunnels and guardrails, the health of structures such as bridges, tunnels and guardrails directly affects the safety and service life of the highway.

[0004] Furthermore, in the process of monitoring the above-mentioned bridges, tunnels, guardrails and other structures, the existing technology mostly relies on manual monitoring by staff. This monitoring method is inefficient and has limited accuracy, which makes it difficult to accurately obtain the status information of the structure, thereby reducing work efficiency. Summary of the invention

[0005] Based on this, the purpose of the present invention is to provide an object detection method and system for highways, so as to solve the problem that the monitoring efficiency of structures in the prior art is low, resulting in difficulty in accurately obtaining structure status information.

[0006] The first aspect of the embodiment of the present invention proposes:

[0007] A method for detecting an object on a highway, wherein the method comprises:

[0008] The real-time physical parameters of the structure located on the highway are collected in real time by a first preset collection device, and the real-scene image corresponding to the structure is collected in real time by a second preset collection device;

[0009] Performing fusion processing on the real-time physical parameters and the real-scene images to generate corresponding monitoring data sets in real time, and inputting the monitoring data sets into a preset data processing model accordingly;

[0010] Performing corresponding quantitative processing on the monitoring data set through the preset data processing model to generate corresponding target detection values ​​in real time, and judging in real time whether the target detection value is greater than a preset detection threshold;

[0011] If it is determined in real time that the target detection value is greater than the preset detection threshold, corresponding alarm information is generated in real time, and corresponding maintenance information is generated in real time.

[0012] The beneficial effect of the present invention is that the corresponding status information can be indirectly obtained by collecting the real-time physical parameters and real-scene images of the structures on the highway. Based on this, in order to facilitate subsequent analysis, it is further integrated into the corresponding monitoring data set. Furthermore, it is quantified through a pre-set data processing model, and finally it is judged whether the target detection value is greater than the preset detection threshold, and the corresponding information is output according to the judgment result, so that the detection of the structure can be completed accurately, which correspondingly improves the work efficiency.

[0013] Furthermore, the step of fusing the real-time physical parameters and the real-scene image to generate a corresponding monitoring data set in real time includes:

[0014] When the real-time physical parameters are acquired in real time, a full scan is performed on the real-time physical parameters to detect correspondingly several parameter types contained in the real-time physical parameters;

[0015] A parameter subset corresponding to each parameter type is matched in the real-time physical parameters, and the real scene image and each parameter subset are fused to generate the monitoring data set accordingly, and each parameter subset is unique.

[0016] Furthermore, the step of fusing the real scene image and each of the parameter subsets to correspondingly generate the monitoring data set includes:

[0017] When each of the parameter subsets is acquired in real time, a number of original parameter values ​​respectively included in each of the parameter subsets are detected in real time;

[0018] Calculating the parameter average values ​​corresponding to each of the parameter subsets according to the plurality of original parameter values, and integrating each of the parameter average values ​​to construct a corresponding parameter data chain in real time;

[0019] The parameter data chain and the real scene image are fused to generate the monitoring data set accordingly, and the parameter data chain is unique.

[0020] Furthermore, the step of fusing the parameter data link and the real scene image to correspondingly generate the monitoring data set includes:

[0021] When the real scene image is acquired in real time, the real scene image is analyzed and processed to detect the corresponding image attributes and image format in real time;

[0022] Detecting the starting point and the ending point corresponding to the parameter data link in real time, and splicing the image attributes at the starting point and the image format at the ending point, so as to generate the corresponding target data link in real time;

[0023] The target data chain is converted and processed by a preset DTW algorithm to generate the monitoring data set in real time, and the target data chain is unique.

[0024] Furthermore, the step of generating corresponding maintenance information in real time includes:

[0025] When the maintenance information is acquired in real time, a corresponding detection report is generated in real time based on the maintenance information;

[0026] The detection report is dynamically encrypted to generate a corresponding encrypted detection report in real time, and the encrypted detection report is sent to the user terminal of the staff accordingly.

[0027] Furthermore, the step of dynamically encrypting the test report to generate a corresponding encrypted test report in real time includes:

[0028] When the test report is obtained in real time, the test report is fully scanned to detect in real time a number of numbers and letters corresponding to the test report;

[0029] A corresponding encryption key is generated in real time according to the plurality of numbers and the plurality of letters, and the detection report is encrypted by the encryption key to generate the encrypted detection report accordingly.

[0030] Furthermore, the step of generating corresponding encryption keys in real time according to the plurality of numbers and the plurality of letters comprises:

[0031] When the plurality of numbers and the plurality of letters are obtained respectively, the plurality of numbers and the plurality of letters are randomly arranged and combined to generate a plurality of corresponding serial numbers in real time;

[0032] A target serial number is randomly selected from a plurality of the serial numbers as the encryption key, and each of the serial numbers is unique.

[0033] The second aspect of the embodiment of the present invention proposes:

[0034] An object detection system for a highway, wherein the system comprises:

[0035] A collection module, used to collect real-time physical parameters of structures located on the highway through a first preset collection device, and to collect real-time images corresponding to the structures through a second preset collection device;

[0036] A fusion module, used for fusing the real-time physical parameters and the real-scene images to generate corresponding monitoring data sets in real time, and inputting the monitoring data sets into a preset data processing model;

[0037] A judgment module, used to perform corresponding quantitative processing on the monitoring data set through the preset data processing model to generate a corresponding target detection value in real time, and to judge in real time whether the target detection value is greater than a preset detection threshold;

[0038] The output module is used to generate corresponding alarm information in real time and corresponding maintenance information in real time if it is determined in real time that the target detection value is greater than the preset detection threshold.

[0039] Furthermore, the fusion module is specifically used for:

[0040] When the real-time physical parameters are acquired in real time, a full scan is performed on the real-time physical parameters to detect correspondingly several parameter types contained in the real-time physical parameters;

[0041] A parameter subset corresponding to each parameter type is matched in the real-time physical parameters, and the real scene image and each parameter subset are fused to generate the monitoring data set accordingly, and each parameter subset is unique.

[0042] Furthermore, the fusion module is specifically used for:

[0043] When each of the parameter subsets is acquired in real time, a number of original parameter values ​​respectively included in each of the parameter subsets are detected in real time;

[0044] Calculating the parameter average values ​​corresponding to each of the parameter subsets according to the plurality of original parameter values, and integrating each of the parameter average values ​​to construct a corresponding parameter data chain in real time;

[0045] The parameter data chain and the real scene image are fused to generate the monitoring data set accordingly, and the parameter data chain is unique.

[0046] Furthermore, the fusion module is specifically used for:

[0047] When the real scene image is acquired in real time, the real scene image is analyzed and processed to detect the corresponding image attributes and image format in real time;

[0048] Detecting the starting point and the ending point corresponding to the parameter data link in real time, and splicing the image attributes at the starting point and the image format at the ending point, so as to generate the corresponding target data link in real time;

[0049] The target data chain is converted and processed by a preset DTW algorithm to generate the monitoring data set in real time, and the target data chain is unique.

[0050] Furthermore, the output module is specifically used for:

[0051] When the maintenance information is acquired in real time, a corresponding detection report is generated in real time based on the maintenance information;

[0052] The detection report is dynamically encrypted to generate a corresponding encrypted detection report in real time, and the encrypted detection report is sent to the user terminal of the staff accordingly.

[0053] Furthermore, the output module is specifically used for:

[0054] When the test report is obtained in real time, the test report is fully scanned to detect in real time a number of numbers and letters corresponding to the test report;

[0055] A corresponding encryption key is generated in real time according to the plurality of numbers and the plurality of letters, and the detection report is encrypted by the encryption key to generate the encrypted detection report accordingly.

[0056] Furthermore, the output module is specifically used for:

[0057] When the plurality of numbers and the plurality of letters are obtained respectively, the plurality of numbers and the plurality of letters are randomly arranged and combined to generate a plurality of corresponding serial numbers in real time;

[0058] A target serial number is randomly selected from a plurality of the serial numbers as the encryption key, and each of the serial numbers is unique.

[0059] The third aspect of the embodiment of the present invention proposes:

[0060] A computer comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor implements the above-mentioned object detection method for a highway when executing the computer program.

[0061] The fourth aspect of the embodiments of the present invention proposes:

[0062] A readable storage medium stores a computer program, wherein when the program is executed by a processor, the object detection method for a highway as described above is implemented.

[0063] Additional aspects and advantages of the present invention will be given in part in the following description and in part will be obvious from the following description, or will be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] Figure 1 A flowchart of a method for detecting an object on a highway provided by a first embodiment of the present invention;

[0065] Figure 2 This is a structural block diagram of a method for detecting objects on a highway provided in the third embodiment of the present invention.

[0066] The following specific implementation manner will further illustrate the present invention in conjunction with the above-mentioned drawings. DETAILED DESCRIPTION

[0067] In order to facilitate the understanding of the present invention, the present invention will be described more fully below with reference to the relevant drawings. Several embodiments of the present invention are given in the drawings. However, the present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0068] It should be noted that when an element is referred to as being "fixed to" another element, it may be directly on the other element or there may be a central element. When an element is considered to be "connected to" another element, it may be directly connected to the other element or there may be a central element at the same time. The terms "vertical", "horizontal", "left", "right" and similar expressions used herein are for illustrative purposes only.

[0069] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein 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. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0070] See also Figure 1 , which is shown as an object detection method for a highway provided by the first embodiment of the present invention. The object detection method for a highway provided by this embodiment can accurately detect the status information of structures on the highway, thereby correspondingly improving work efficiency.

[0071] Specifically, this embodiment provides:

[0072] A method for detecting an object on a highway comprises the following steps:

[0073] Step S10, real-time physical parameters of structures located on the highway are collected in real time by a first preset collection device, and real-time images corresponding to the structures are collected in real time by a second preset collection device;

[0074] Step S20, fusing the real-time physical parameters and the real-scene image to generate a corresponding monitoring data set in real time, and inputting the monitoring data set into a preset data processing model;

[0075] Step S30, performing corresponding quantitative processing on the monitoring data set through the preset data processing model to generate a corresponding target detection value in real time, and judging in real time whether the target detection value is greater than a preset detection threshold;

[0076] Step S40: If it is determined in real time that the target detection value is greater than the preset detection threshold, corresponding alarm information is generated in real time, and corresponding maintenance information is generated in real time.

[0077] Specifically, in this embodiment, it should be noted that, in order to accurately detect the state information of the structure on the highway, it is necessary to accurately obtain the corresponding parameters and further analyze the corresponding results based on the parameters. Based on this, in order to objectively and accurately collect the required parameters, the present invention will pre-set corresponding sensors inside and on the surface of the structure on the highway, preferably, such as temperature sensors, pressure sensors, and humidity sensors, etc. Based on this, in order to further accurately determine the state information of the structure, in the process of actual application, the present invention will also collect real-life images of the structures on the highway in real time through existing drones for subsequent processing.

[0078] Further, since the information contained in the above-mentioned real-time physical parameters and real-view images is only one-sided, based on this, in order to be able to detect the state information of the current structure in all aspects, the present invention needs to obtain comprehensive information accordingly. Based on this, the present invention will further fuse the current real-time physical parameters and real-view images, and can generate corresponding monitoring data sets in real time. At the same time, the monitoring data sets are input into the pre-set data processing model accordingly. On this basis, the current detection data set can be further quantified through the data processing model, and the required target detection value can be further generated. Specifically, the target detection value is a specific value. Based on this, it is possible to directly determine whether the current target detection value is greater than the preset detection threshold. Specifically, if so, it can be corresponding to the state of the structure corresponding to the current target detection value. There is an abnormality, and it is necessary to issue a corresponding alarm information, and generate corresponding maintenance information in time to perform corresponding maintenance. If not, it can be corresponding to the state of the structure that does not exist. There is an abnormality, so that the state information of the structure can be objectively and accurately detected, and the work efficiency is correspondingly improved.

[0079] Second embodiment

[0080] Furthermore, the step of fusing the real-time physical parameters and the real-scene image to generate a corresponding monitoring data set in real time includes:

[0081] When the real-time physical parameters are acquired in real time, a full scan is performed on the real-time physical parameters to detect correspondingly several parameter types contained in the real-time physical parameters;

[0082] A parameter subset corresponding to each parameter type is matched in the real-time physical parameters, and the real scene image and each parameter subset are fused to generate the monitoring data set accordingly, and each parameter subset is unique.

[0083] Furthermore, the step of fusing the real scene image and each of the parameter subsets to correspondingly generate the monitoring data set includes:

[0084] When each of the parameter subsets is acquired in real time, a number of original parameter values ​​respectively included in each of the parameter subsets are detected in real time;

[0085] Calculating the parameter average values ​​corresponding to each of the parameter subsets according to the plurality of original parameter values, and integrating each of the parameter average values ​​to construct a corresponding parameter data chain in real time;

[0086] The parameter data chain and the real scene image are fused to generate the monitoring data set accordingly, and the parameter data chain is unique.

[0087] Furthermore, the step of fusing the parameter data link and the real scene image to correspondingly generate the monitoring data set includes:

[0088] When the real scene image is acquired in real time, the real scene image is analyzed and processed to detect the corresponding image attributes and image format in real time;

[0089] Detecting the starting point and the ending point corresponding to the parameter data link in real time, and splicing the image attributes at the starting point and the image format at the ending point, so as to generate the corresponding target data link in real time;

[0090] The target data chain is converted and processed by a preset DTW algorithm to generate the monitoring data set in real time, and the target data chain is unique.

[0091] Furthermore, the step of generating corresponding maintenance information in real time includes:

[0092] When the maintenance information is acquired in real time, a corresponding detection report is generated in real time based on the maintenance information;

[0093] The detection report is dynamically encrypted to generate a corresponding encrypted detection report in real time, and the encrypted detection report is sent to the user terminal of the staff accordingly.

[0094] Furthermore, the step of dynamically encrypting the test report to generate a corresponding encrypted test report in real time includes:

[0095] When the test report is obtained in real time, the test report is fully scanned to detect in real time a number of numbers and letters corresponding to the test report;

[0096] A corresponding encryption key is generated in real time according to the plurality of numbers and the plurality of letters, and the detection report is encrypted by the encryption key to generate the encrypted detection report accordingly.

[0097] Furthermore, the step of generating corresponding encryption keys in real time according to the plurality of numbers and the plurality of letters comprises:

[0098] When the plurality of numbers and the plurality of letters are obtained respectively, the plurality of numbers and the plurality of letters are randomly arranged and combined to generate a plurality of corresponding serial numbers in real time;

[0099] A target serial number is randomly selected from a plurality of the serial numbers as the encryption key, and each of the serial numbers is unique.

[0100] In addition, in this embodiment, it is also necessary to explain that after the required parameters and real-life images are obtained in real time through the above steps, in order to objectively and accurately complete the fusion processing between the two, it is necessary to accurately obtain the information contained in the two. Preferably, the present invention will first perform a full scan of the current real-time physical parameters, and can further detect several parameter types corresponding to the current real-time physical parameters, specifically, such as temperature parameters, humidity parameters, and pressure parameters. Based on this, the present invention will further match the parameter subsets corresponding to each current parameter type in the current real-time physical parameters, wherein it should be pointed out that the parameters contained in each parameter subset are specific values, so that the parameter average value corresponding to each current parameter subset can be further calculated. Specifically, the parameter average value can represent the characteristics of each parameter type. Based on this, the present invention will further integrate the current average values ​​of each parameter, and can correspondingly construct the required parameter data chain. Specifically, the parameter data chain can represent the characteristics of the above-mentioned real-time physical parameters. Based on this, the present invention will further parse the above-mentioned real-scene images, and can parse out the required image attributes and image formats accordingly, that is, the current image attributes and image formats can correspond to the characteristics of the current real-scene images, and on this basis, subsequent fusion processing can be performed. Preferably, the present invention will intersperse the current image attributes at the starting point of the above-mentioned parameter data chain, and correspondingly, intersperse the above-mentioned image formats at the ending point of the above-mentioned parameter data chain, so as to generate the corresponding target data chain. Based on this, it is only necessary to convert the current target data chain into the corresponding numerical value to obtain the required monitoring data set, so as to facilitate subsequent processing.

[0101] Furthermore, after obtaining the required monitoring data set in real time through the above steps, the present invention can objectively and accurately complete subsequent judgments based on the monitoring data set. If it is judged in real time that the target detection value is greater than the preset detection threshold, it is necessary to immediately issue a corresponding alarm message. Otherwise, there is no need to issue an alarm message. It should be pointed out that after each detection is completed, the present invention will generate a corresponding detection report. Furthermore, in order to prevent the data in the detection report from being leaked, the current detection report will be dynamically encrypted, and a corresponding encrypted detection report can be generated, which can eventually be sent to the user terminal of the staff, further improving work efficiency while improving the user experience.

[0102] See also Figure 2 , the third embodiment of the present invention provides:

[0103] An object detection system for a highway, wherein the system comprises:

[0104] A collection module, used to collect real-time physical parameters of structures located on the highway through a first preset collection device, and to collect real-time images corresponding to the structures through a second preset collection device;

[0105] A fusion module, used for fusing the real-time physical parameters and the real-scene images to generate corresponding monitoring data sets in real time, and inputting the monitoring data sets into a preset data processing model;

[0106] A judgment module, used to perform corresponding quantitative processing on the monitoring data set through the preset data processing model to generate a corresponding target detection value in real time, and to judge in real time whether the target detection value is greater than a preset detection threshold;

[0107] The output module is used to generate corresponding alarm information in real time and corresponding maintenance information in real time if it is determined in real time that the target detection value is greater than the preset detection threshold.

[0108] Furthermore, the fusion module is specifically used for:

[0109] When the real-time physical parameters are acquired in real time, a full scan is performed on the real-time physical parameters to detect correspondingly several parameter types contained in the real-time physical parameters;

[0110] A parameter subset corresponding to each parameter type is matched in the real-time physical parameters, and the real scene image and each parameter subset are fused to generate the monitoring data set accordingly, and each parameter subset is unique.

[0111] Furthermore, the fusion module is specifically used for:

[0112] When each of the parameter subsets is acquired in real time, a number of original parameter values ​​respectively included in each of the parameter subsets are detected in real time;

[0113] Calculating the parameter average values ​​corresponding to each of the parameter subsets according to the plurality of original parameter values, and integrating each of the parameter average values ​​to construct a corresponding parameter data chain in real time;

[0114] The parameter data chain and the real scene image are fused to generate the monitoring data set accordingly, and the parameter data chain is unique.

[0115] Furthermore, the fusion module is specifically used for:

[0116] When the real scene image is acquired in real time, the real scene image is analyzed and processed to detect the corresponding image attributes and image format in real time;

[0117] Detecting the starting point and the ending point corresponding to the parameter data link in real time, and splicing the image attributes at the starting point and the image format at the ending point, so as to generate the corresponding target data link in real time;

[0118] The target data chain is converted and processed by a preset DTW algorithm to generate the monitoring data set in real time, and the target data chain is unique.

[0119] Furthermore, the output module is specifically used for:

[0120] When the maintenance information is acquired in real time, a corresponding detection report is generated in real time based on the maintenance information;

[0121] The detection report is dynamically encrypted to generate a corresponding encrypted detection report in real time, and the encrypted detection report is sent to the user terminal of the staff accordingly.

[0122] Furthermore, the output module is specifically used for:

[0123] When the test report is obtained in real time, the test report is fully scanned to detect in real time a number of numbers and letters corresponding to the test report;

[0124] A corresponding encryption key is generated in real time according to the plurality of numbers and the plurality of letters, and the detection report is encrypted by the encryption key to generate the encrypted detection report accordingly.

[0125] Furthermore, the output module is specifically used for:

[0126] When the plurality of numbers and the plurality of letters are obtained respectively, the plurality of numbers and the plurality of letters are randomly arranged and combined to generate a plurality of corresponding serial numbers in real time;

[0127] A target serial number is randomly selected from a plurality of the serial numbers as the encryption key, and each of the serial numbers is unique.

[0128] A fourth embodiment of the present invention provides a computer, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the object detection method for a highway as described above when executing the computer program.

[0129] A fifth embodiment of the present invention provides a readable storage medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the object detection method for a highway as described above.

[0130] In summary, the object detection method and system for highways provided by the above embodiments of the present invention can accurately detect the status information of structures on the highways, thereby correspondingly improving work efficiency.

[0131] It should be noted that the above modules can be functional modules or program modules, and can be implemented by software or hardware. For modules implemented by hardware, the above modules can be located in the same processor; or the above modules can be located in different processors in any combination.

[0132] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0133] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0134] It should be understood that the various parts of the present invention can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, a plurality of steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0135] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "examples", "specific examples", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present invention. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0136] The above-described embodiments only express several implementation methods of the present invention, and the description thereof is relatively specific and detailed, but it cannot be understood as limiting the scope of the present invention. It should be pointed out that, for a person of ordinary skill in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the attached claims.

Claims

1. A method for detecting an object on a highway, characterized in that: The method comprises: The real-time physical parameters of the structure located on the highway are collected in real time by a first preset collection device, and the real-scene image corresponding to the structure is collected in real time by a second preset collection device; Performing fusion processing on the real-time physical parameters and the real-scene images to generate corresponding monitoring data sets in real time, and inputting the monitoring data sets into a preset data processing model accordingly; Performing corresponding quantitative processing on the monitoring data set through the preset data processing model to generate corresponding target detection values ​​in real time, and judging in real time whether the target detection value is greater than a preset detection threshold; If it is determined in real time that the target detection value is greater than the preset detection threshold, corresponding alarm information is generated in real time, and corresponding maintenance information is generated in real time.

2. The object detection method for highway according to claim 1, characterized in that: The step of fusing the real-time physical parameters and the real-scene image to generate a corresponding monitoring data set in real time includes: When the real-time physical parameters are acquired in real time, a full scan is performed on the real-time physical parameters to detect correspondingly several parameter types contained in the real-time physical parameters; A parameter subset corresponding to each parameter type is matched in the real-time physical parameters, and the real scene image and each parameter subset are fused to generate the monitoring data set accordingly, and each parameter subset is unique.

3. The object detection method for highway according to claim 2, characterized in that: The step of fusing the real scene image and each of the parameter subsets to correspondingly generate the monitoring data set includes: When each of the parameter subsets is acquired in real time, a number of original parameter values ​​respectively included in each of the parameter subsets are detected in real time; Calculating the parameter average values ​​corresponding to each of the parameter subsets according to the plurality of original parameter values, and integrating each of the parameter average values ​​to construct a corresponding parameter data chain in real time; The parameter data chain and the real scene image are fused to generate the monitoring data set accordingly, and the parameter data chain is unique.

4. The object detection method for highway according to claim 3, characterized in that: The step of fusing the parameter data link and the real scene image to correspondingly generate the monitoring data set includes: When the real scene image is acquired in real time, the real scene image is analyzed and processed to detect the corresponding image attributes and image format in real time; Detecting the starting point and the ending point corresponding to the parameter data link in real time, and splicing the image attributes at the starting point and the image format at the ending point, so as to generate the corresponding target data link in real time; The target data chain is converted and processed by a preset DTW algorithm to generate the monitoring data set in real time, and the target data chain is unique.

5. The object detection method for highway according to claim 1, characterized in that: The step of generating corresponding maintenance information in real time comprises: When the maintenance information is acquired in real time, a corresponding detection report is generated in real time based on the maintenance information; The detection report is dynamically encrypted to generate a corresponding encrypted detection report in real time, and the encrypted detection report is sent to the user terminal of the staff accordingly.

6. The object detection method for highway according to claim 5, characterized in that: The step of dynamically encrypting the test report to generate a corresponding encrypted test report in real time includes: When the test report is obtained in real time, the test report is fully scanned to detect in real time a number of numbers and letters corresponding to the test report; A corresponding encryption key is generated in real time according to the plurality of numbers and the plurality of letters, and the detection report is encrypted by the encryption key to generate the encrypted detection report accordingly.

7. The object detection method for highway according to claim 6, characterized in that: The step of generating corresponding encryption keys in real time according to the plurality of numbers and the plurality of letters comprises: When the plurality of numbers and the plurality of letters are obtained respectively, the plurality of numbers and the plurality of letters are randomly arranged and combined to generate a plurality of corresponding serial numbers in real time; A target serial number is randomly selected from a plurality of the serial numbers as the encryption key, and each of the serial numbers is unique.

8. An object detection system for a highway, characterized in that: The system comprises: A collection module, used to collect real-time physical parameters of structures located on the highway through a first preset collection device, and to collect real-time images corresponding to the structures through a second preset collection device; A fusion module, used for fusing the real-time physical parameters and the real-scene images to generate corresponding monitoring data sets in real time, and inputting the monitoring data sets into a preset data processing model; A judgment module, used to perform corresponding quantitative processing on the monitoring data set through the preset data processing model to generate a corresponding target detection value in real time, and to judge in real time whether the target detection value is greater than a preset detection threshold; The output module is used to generate corresponding alarm information in real time and corresponding maintenance information in real time if it is determined in real time that the target detection value is greater than the preset detection threshold.

9. A computer comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that: When the processor executes the computer program, the object detection method for a highway as claimed in any one of claims 1 to 7 is implemented.

10. A readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the object detection method for a highway as claimed in any one of claims 1 to 7 is implemented.

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