Dam termite nest positioning method, device and equipment and storage medium

Abstract

The invention relates to the technical field of water conservancy projects, in particular to a dam termite nest positioning method, device and equipment and a storage medium. Termite activity identification is carried out on collected termite site images through a trained termite identification model in an image identification mode so as to determine at least one camera shooting site with termite activity, and finally intersection analysis is carried out according to position information of the camera shooting site with termite activity. According to the method, the reliability of the conclusion of judging the termite activity area is improved, so that termite nest positioning is achieved with small workload, omnibearing detection of a dam is achieved, nest searching efficiency is high, and the technical problems that in the prior art, termite nest positioning efficiency is low, and accuracy is not high are solved.

Description

Technical Field

[0001] The invention relates to the technical field of water conservancy engineering, and in particular to a method, a device, equipment and a storage medium for locating termite nests on dams. Background Art

[0002] Termites are one of the most important animals that harm dikes in southern China. Termites build nests, build roads and reproduce on dikes. Termites build nests and reproduce in the main body of water conservancy projects, and build ant roads that extend in all directions. This can easily lead to leakage, falls and other dangerous situations in the project. Severe cases can even cause dam collapse and dike collapse. They are a major biological hazard that affects the safety of water conservancy projects such as reservoir dams and dikes, and pose a major risk to the safe operation of reservoir dams, dikes and other projects.

[0003] Methods for locating termite nests include manual observation or ground indicators such as mud lines and cluster holes to determine the trajectory of termite activity, finding nests through characteristic vents and excrement, and physical detection methods such as ground penetrating radar based on electromagnetic waves, resistivity, and high-density electrical detection.

[0004] However, manual nest locating is labor-intensive and inaccurate, and digging the nest soil will cause great damage to the dam body. Using electromagnetic or resistivity methods, the soil around the nest is uneven, the water content varies greatly, and the nest structure and material variability are also large, resulting in a high degree of uncertainty in detection. In addition, if a complete nest search is performed once, the workload is large and the cost is relatively high. At present, there is no method that can quickly and accurately locate the location of termite nests.

[0005] The above contents are only used to assist in understanding the technical solution of the present invention and do not constitute an admission that the above contents are prior art. Summary of the invention

[0006] The main purpose of the present invention is to provide a method, device, equipment and storage medium for locating termite nests on a dam, aiming to solve the technical problems of low efficiency and low accuracy in locating termite nests in the prior art.

[0007] To achieve the above object, the present invention provides a method for locating termite nests on a dam, the method comprising the following steps:

[0008] Periodically collect images of several termite sites in the dam area to be inspected;

[0009] Using the trained termite recognition model to identify termite activities on the plurality of termite site images, so as to determine at least one target camera site, wherein the target camera site is a camera site where termite activities exist;

[0010] An intersection analysis is performed based on the location information of the target camera site to obtain the location of the target termite nest.

[0011] Optionally, performing intersection analysis according to the location information of the target camera site to obtain the location of the target termite nest includes:

[0012] Extracting coordinate information of the target camera site according to the location information of the target camera site;

[0013] Generate candidate areas for each target camera station based on the coordinate information;

[0014] Determine the intersection area of ​​each candidate area and obtain the location of the target termite nest.

[0015] Optionally, determining the intersection area of ​​each candidate area to obtain the target termite nest location includes:

[0016] Determine at least one intersection region between the candidate regions to obtain at least one candidate termite nest location;

[0017] Determining the number of common overlapping regions corresponding to the at least one candidate termite nest location;

[0018] A credibility analysis is performed on the at least one candidate termite nest location based on the number of common overlapping regions to obtain a target termite nest location.

[0019] Optionally, performing a credibility analysis on the at least one candidate termite nest location based on the number of common overlapping regions to obtain the target termite nest location includes:

[0020] Sorting the candidate termite nest locations based on the number of common overlapping areas;

[0021] The target termite nest location is determined based on the sorted candidate termite nest locations.

[0022] Optionally, the plurality of termite site images include at least a first termite site image and a second termite site image continuously captured by the same camera station;

[0023] The step of identifying termite activities by using a trained termite identification model on the plurality of termite site images to determine at least one termite activity site includes:

[0024] Determine the location information of the camera site to be detected based on the first termite site image and the second termite site image;

[0025] The first termite site image and the second termite site image are respectively subjected to termite activity recognition by using a trained termite recognition model to determine whether there is termite activity at the camera site to be detected;

[0026] At least one termite activity site is generated according to the identification result.

[0027] Optionally, the performing termite activity recognition on the first termite site image and the second termite site image through a trained termite recognition model to determine whether there is termite activity at the camera site to be detected includes:

[0028] extracting a first image noise residue of the first termite site image and a second image noise residue of the second termite site image respectively;

[0029] determining a first similarity of a first termite site image according to the first image noise residue and a preset image noise residue;

[0030] determining a second similarity of the second termite site image according to the second image noise residue and a preset image noise residue;

[0031] When both the first similarity and the second similarity are smaller than a preset similarity threshold, it is determined that termite activity exists at the camera site to be detected.

[0032] Optionally, the method for locating termite nests on a dam further includes:

[0033] Calling a preset program interface to mark the area where the target termite nest is located;

[0034] The marked image information of the dam area to be inspected is displayed to the user to provide navigation assistance information.

[0035] In addition, to achieve the above-mentioned purpose, the present invention also proposes a locating device for termite nests in dams, and the locating device for termite nests in dams comprises:

[0036] A collection module, used for periodically collecting images of several termite sites in the dam area to be detected;

[0037] An identification module, used to identify termite activities by using a trained termite identification model on the plurality of termite site images to determine at least one target camera site, wherein the target camera site is a camera site where termite activities exist;

[0038] The analysis module is used to perform intersection analysis based on the location information of the target camera site to obtain the location of the target termite nest.

[0039] In addition, to achieve the above-mentioned purpose, the present invention also proposes a locating device for a dam termite nest, the locating device for a dam termite nest comprising: a memory, a processor, and a locating program for a dam termite nest stored in the memory and executable on the processor, the locating program for a dam termite nest being configured to implement the steps of the locating method for a dam termite nest as described above.

[0040] In addition, to achieve the above-mentioned purpose, the present invention also proposes a storage medium, on which a locating program for dam termite nests is stored. When the locating program for dam termite nests is executed by a processor, the steps of the locating method for dam termite nests as described above are implemented.

[0041] In addition, to achieve the above-mentioned purpose, the present application also provides a computer program product, which includes a computer program. When the computer program is executed by a processor, the steps of the method for locating the nest of dam termites as described above are implemented.

[0042] One or more technical solutions proposed by the present invention have at least the following technical effects: the present invention discloses a method for locating termite nests on a dam, the method comprising: periodically collecting a number of termite site images in a dam area to be detected; performing termite activity recognition on the several termite site images through a trained termite recognition model to determine at least one target camera site, the target camera site being a camera site where termite activity exists; performing intersection analysis according to the position information of the target camera site to obtain the target termite nest position. Compared with the prior art, the present invention periodically collects a number of termite site images in a dam area to be detected, and performs termite activity recognition on the collected termite site images through a trained termite recognition model through image recognition to determine at least one camera site where termite activity exists; finally, performing intersection analysis according to the position information of the camera site where termite activity exists, thereby improving the reliability of the conclusion of judging the existence of termite activity areas, thereby realizing termite nest positioning with a smaller workload, and realizing all-round detection of the dam, with higher nest-finding efficiency, and avoiding the technical problems of low efficiency and low accuracy in locating termite nests in the prior art. BRIEF DESCRIPTION OF THE DRAWINGS

[0043] The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the present application and, together with the description, serve to explain the principles of the present application.

[0044] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, for ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative labor.

[0045] Figure 1 It is a schematic diagram of the process of the first embodiment of the method for locating termite nests on a dam of the present invention;

[0046] Figure 2 A schematic diagram of the architecture of locating a termite nest on a dam according to an embodiment of a method for locating a termite nest on a dam of the present invention;

[0047] Figure 3 It is a schematic diagram of the flow chart of the second embodiment of the method for locating termite nests on dams of the present invention;

[0048] Figure 4 It is a schematic diagram of an example of locating a termite nest in a dam according to an embodiment of the method for locating a termite nest of the present invention;

[0049] Figure 5 It is a schematic diagram of the process of the third embodiment of the method for locating termite nests on a dam of the present invention;

[0050] Figure 6 It is a structural block diagram of a first embodiment of a device for locating termite nests on dams according to the present invention;

[0051] Figure 7 It is a structural schematic diagram of a locating device for termite nests on a dam in a hardware operating environment involved in an embodiment of the present invention.

[0052] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0053] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of the present application and are not used to limit the present application.

[0054] In order to better understand the technical solution of the present application, a detailed description will be given below in conjunction with the accompanying drawings and specific implementation methods.

[0055] Based on this, the embodiment of the present invention provides a method for locating termite nests on a dam, referring to Figure 1 , Figure 1 It is a schematic flow chart of a first embodiment of a method for locating termite nests on a dam according to the present invention.

[0056] In this embodiment, the method for locating the dam termite nest includes:

[0057] Step S10: Periodically collect images of several termite sites in the dam area to be detected.

[0058] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or an electronic device capable of realizing the above functions, a control computer and a server, etc. The following takes the control computer as an example to illustrate this embodiment and the following embodiments.

[0059] It should be understood that the traditional detection of termite nests in water conservancy projects such as dams is generally through electromagnetic signals, pulse signals, etc. Specifically, taking pulse signals as an example, the traditional technology generally determines the center point and multiple corner points in the dam test area, so that the time difference between the moment when the sound sensor receives the sound pulse signal at each of the corner points and the moment when the sound pulse signal is received at the center point is the same, and then the location of the termite nest is determined based on the position of the center point, the position of each of the corner points and the time difference. However, in this method, the pulse signal will be interfered by the external environment, for example, debris or gaps on the dam, resulting in inaccurate positioning results, and the frequency of the pulse signal is generally low, and the transmission distance is limited. In the positioning process, it is necessary to search one area after another, and the amount of repetitive work is large.

[0060] Based on this, this embodiment proposes a method for locating termite nests on a dam, and the method for locating termite nests on a dam includes: periodically collecting a number of termite site images in the dam area to be detected; using the several termite site images to identify termite activities through a trained termite recognition model to determine at least one target camera site, and the target camera site is a camera site where termite activities exist; performing intersection analysis based on the location information of the target camera site to obtain the location of the target termite nest, thereby solving the impact of the external environment on the positioning process and reducing repeated and ineffective work.

[0061] refer to Figure 2 In this embodiment, multiple image acquisition devices are arranged in the dam area to be detected, such as high-definition cameras, 2D or 3D cameras, etc., and the image acquisition devices are powered by independent power modules. Taking the camera as an example, when the camera collects the image of the surrounding termite site, the termite site image can be sent to the Beidou module through the Uart serial port, so as to determine the position information of the camera based on Beidou positioning, so as to perform intersection analysis and obtain the location of the target termite nest.

[0062] It can be understood that the termite site image refers to the image of the area where each image acquisition device is located in the dam area to be detected, among which some termite site images may have traces of termite activity, while some may not. Therefore, it is necessary to subsequently identify termite activities for all termite site images to narrow the nesting range.

[0063] Step S20: performing termite activity recognition on the plurality of termite site images through a trained termite recognition model to determine at least one target camera site, wherein the target camera site is a camera site where termite activity exists.

[0064] It should be understood that the trained termite recognition model can be a neural network classification model based on deep learning. In this embodiment, a deep belief network (DBN) is mainly used, and its architecture is composed of a plurality of restricted Boltzmann machines (RBM) stacked together. It is mainly used to extract the characteristics of termite activity traces in each termite site image, and classify the termite site images, so as to extract images with termite activity traces, thereby determining the camera sites with termite activities. Moreover, since the dam termite nest positioning used in this embodiment will not cause damage to the dam due to factors such as ultrasound and electromagnetic waves, the availability is higher.

[0065] Step S30: performing intersection analysis based on the location information of the target camera site to obtain the location of the target termite nest.

[0066] It should be understood that in the present embodiment, the camera stations arranged on the dam to be inspected are evenly arranged, and due to the influence of the living habits of termites, termites will only move within a certain distance from the ant nest. Therefore, the more termite traces there are in an area, the greater the probability of the existence of an ant nest. The termite site images collected by the camera stations may have overlapping areas. Therefore, the same termite activity trace may appear in multiple termite site images collected by multiple camera stations. By performing intersection analysis on the camera stations with termite activity traces, the area with the highest probability of the existence of a termite nest can be determined, thereby locating the ant nest.

[0067] Furthermore, the method for locating termite nests on a dam further includes:

[0068] Calling a preset program interface to mark the area where the target termite nest is located;

[0069] The marked image information of the dam area to be inspected is displayed to the user to provide navigation assistance information.

[0070] In this embodiment, in order to eliminate the damage caused by termites to the dam, after the location of the target termite nest is determined, the area where the target termite nest is located can be marked by calling a preset program interface, providing navigation assistance information for users to destroy the termite nest, thereby improving the safety of the dam.

[0071] Specifically, the specific code for marking the area where the target termite nest is located is:

[0072] def plot_circles_and_intersections(circles,intersections):

[0073] fig,ax=plt.subplots()

[0074] for circle in circles:

[0075] c=plt.Circle(circle['center'],circle['radius'],fill=False)

[0076] ax.add_artist(c)

[0077] for point in intersections:

[0078] ax.plot(point.x,point.y,'bo')#Mark the intersection with a blue dot

[0079] plt.show()

[0080] #Call the function and draw

[0081] intersections=find_common_intersection(circles)

[0082] plot_circles_and_intersections(circles,intersections).

[0083] This embodiment periodically collects several termite site images in the dam area to be detected, and uses image recognition to identify termite activities in the collected termite site images through a trained termite recognition model to determine at least one camera site where termite activities exist. Finally, intersection analysis is performed based on the position information of the camera sites where termite activities are determined to exist, thereby improving the reliability of the conclusion of the existence of termite activity areas, thereby locating termite nests with a smaller workload, and realizing all-round detection of the dam, with higher nest-finding efficiency, avoiding the technical problems of low efficiency and low accuracy in locating termite nests in the prior art.

[0084] Based on the first embodiment of the present application, in the second embodiment of the present application, the same or similar contents as those in the above-mentioned embodiment 1 can be referred to the above introduction, and will not be repeated in the following. Figure 3 , step S30, comprising:

[0085] Step S301: extracting the coordinate information of the target camera site according to the position information of the target camera site.

[0086] Step S302: generating candidate areas of each target camera station based on the coordinate information.

[0087] Step S303: determine the intersection area of ​​each candidate area to obtain the target termite nest location.

[0088] It should be noted that the location information of the target camera site can be determined by the Beidou positioning system or by the number of each camera site, and the coordinate information of the target camera site refers to the coordinates of the camera site in the world coordinate system.

[0089] It can be understood that the candidate area is an area of ​​a certain shape set with the target camera station as the center point, and the shape of the candidate area includes but is not limited to a circle, a square and other shapes. Figure 4 In this embodiment, a circular candidate area is used as an example for explanation. Specifically, if termite sites 3, 4, and 5 all detect traces of termite activity, then the probability that a termite nest exists at the intersection of the candidate areas corresponding to the three sites is relatively high. By performing intersection processing on the candidate areas, the location of the termite nest can be greatly reduced.

[0090] Furthermore, the determining of the intersection area of ​​each candidate area to obtain the target termite nest location includes:

[0091] Determine at least one intersection region between the candidate regions to obtain at least one candidate termite nest location;

[0092] Determining the number of common overlapping regions corresponding to the at least one candidate termite nest location;

[0093] A credibility analysis is performed on the at least one candidate termite nest location based on the number of common overlapping regions to obtain a target termite nest location.

[0094] In a specific implementation, the number of common overlapping regions refers to the number of candidate regions occupied by the candidate termite nest locations, for example: Figure 4 The number of common overlapping areas corresponding to the blue marked area is 3, which occupies the candidate areas corresponding to the three camera sites. Since there may be multiple intersection areas between the candidate areas in a map, in order to improve the accuracy of positioning, this embodiment can screen each candidate termite nest location according to the number of candidate areas occupied by the intersection area, so that the candidate termite nest location that occupies the most candidate areas corresponding to the camera sites is used as the target termite nest location, thereby improving the accuracy of termite nest positioning.

[0095] Further, performing a credibility analysis on the at least one candidate termite nest location based on the number of common overlapping regions to obtain the target termite nest location includes:

[0096] Sorting the candidate termite nest locations based on the number of common overlapping areas;

[0097] The target termite nest location is determined based on the sorted candidate termite nest locations.

[0098] It should be understood that in the actual sorting process, there may be a situation where the number of common overlapping areas of multiple candidate termite nest locations is the same, because in the actual scenario, there may be multiple termite nests on the dam. When sorting, only the number of common overlapping areas can be considered, that is, there can be multiple candidate termite nest locations with the same number of common overlapping areas.

[0099] This embodiment extracts the coordinate information of the target camera site according to the position information of the target camera site; generates candidate areas of each target camera site based on the coordinate information; determines the intersection area of ​​each candidate area to obtain the target termite nest position, determines the target camera site with traces of termite activities, divides the candidate area based on the coordinate information of each target camera site, and finally determines the final target termite nest position based on the intersection area of ​​each candidate area.

[0100] Based on the second embodiment of the present application, in the third embodiment of the present application, the same or similar contents as those in the first embodiment can be referred to the above description, and will not be described in detail later. Figure 5 , step S20, comprising:

[0101] Step S201: determining the location information of the camera site to be detected based on the first termite site image and the second termite site image.

[0102] Step S202: performing termite activity recognition on the first termite site image and the second termite site image through the trained termite recognition model to determine whether there is termite activity at the camera site to be detected.

[0103] Step S203: generating at least one termite activity site according to the identification result.

[0104] It should be noted that the first termite site image and the second termite site image refer to the first termite site image and the second termite site image of the same camera site. In terms of time sequence, the first termite site image is earlier than the second termite site image. When the camera is collecting site images, the activity traces of termites may not be obvious, and the detection is difficult. At the same time, due to external light and shadow, occlusion and other reasons, the collected images are blurred, which is not conducive to the subsequent identification of termite activity traces. Therefore, by continuously collecting two termite site images, a comparison can be made to reduce the impact of the external environment.

[0105] Furthermore, the step of performing termite activity recognition on the first termite site image and the second termite site image through a trained termite recognition model to determine whether there is termite activity at the camera site to be detected includes:

[0106] extracting a first image noise residue of the first termite site image and a second image noise residue of the second termite site image respectively;

[0107] determining a first similarity of a first termite site image according to the first image noise residue and a preset image noise residue;

[0108] determining a second similarity of the second termite site image according to the second image noise residue and a preset image noise residue;

[0109] When both the first similarity and the second similarity are smaller than a preset similarity threshold, it is determined that termite activity exists at the camera site to be detected.

[0110] In a specific implementation, image noise residue refers to the data content remaining after image removal based on the original dam image. The original dam image refers to the dam image without any traces of termite activity in the area. By removing the original dam image, only the remaining traces of termite activity can be marked, so as to perform similarity judgment in combination with the image noise residues corresponding to the two termite site images. Among them, the preset image noise residue refers to the noise residue of the pre-collected termite activity trace sample. By calculating the similarity of the noise residue between the sample and the actual termite site image, it is possible to determine whether there is termite activity at the camera site to be detected, thereby achieving accurate positioning of the termite activity traces.

[0111] This embodiment determines the location information of the camera site to be detected based on the first termite site image and the second termite site image; uses the first termite site image and the second termite site image to identify termite activities through trained termite recognition models to determine whether there is termite activity at the camera site to be detected; generates at least one termite activity site based on the recognition result, identifies termite activity traces through multiple termite site images, reduces interference from the external environment, and improves the accuracy of subsequent positioning.

[0112] This application also provides a locating device for termite nests on dams, please refer to Figure 6 , the locating device for the dam termite nest comprises:

[0113] The acquisition module 10 is used to periodically acquire a number of termite site images in the dam area to be detected.

[0114] The recognition module 20 is used to perform termite activity recognition on the plurality of termite site images through a trained termite recognition model to determine at least one target camera site, where the target camera site is a camera site where termite activity exists.

[0115] The analysis module 30 is used to perform intersection analysis based on the location information of the target camera site to obtain the location of the target termite nest.

[0116] This embodiment periodically collects several termite site images in the dam area to be detected, and uses image recognition to identify termite activities in the collected termite site images through a trained termite recognition model to determine at least one camera site where termite activities exist. Finally, intersection analysis is performed based on the position information of the camera sites where termite activities are determined to exist, thereby improving the reliability of the conclusion of the existence of termite activity areas, thereby locating termite nests with a smaller workload, and realizing all-round detection of the dam, with higher nest-finding efficiency, avoiding the technical problems of low efficiency and low accuracy in locating termite nests in the prior art.

[0117] In one embodiment, the analysis module 30 is further used to extract the coordinate information of the target camera site according to the location information of the target camera site; generate candidate areas of each target camera site based on the coordinate information; determine the intersection area of ​​each candidate area to obtain the target termite nest location.

[0118] In one embodiment, the analysis module 30 is further used to determine at least one intersection area between the candidate areas to obtain at least one candidate termite nest location; determine the number of common overlapping areas of the areas corresponding to the at least one candidate termite nest location; and perform credibility analysis on the at least one candidate termite nest location based on the number of common overlapping areas to obtain the target termite nest location.

[0119] In one embodiment, the analysis module 30 is further used to sort the candidate termite nest locations based on the number of common overlapping regions; and determine the target termite nest location based on the sorted candidate termite nest locations.

[0120] In one embodiment, the recognition module 20 is further used to determine the location information of the camera site to be detected based on the first termite site image and the second termite site image; perform termite activity recognition on the first termite site image and the second termite site image through a trained termite recognition model to determine whether there is termite activity at the camera site to be detected; and generate at least one termite activity site based on the recognition results.

[0121] In one embodiment, the recognition module 20 is further used to extract a first image noise residue of the first termite site image and a second image noise residue of the second termite site image, respectively; determine a first similarity of the first termite site image according to the first image noise residue and a preset image noise residue; determine a second similarity of the second termite site image according to the second image noise residue and a preset image noise residue; and when both the first similarity and the second similarity are less than a preset similarity threshold, determine that termite activity exists at the camera site to be detected.

[0122] In one embodiment, the analysis module 30 is further used to call a preset program interface to mark the area where the target termite nest is located; and to display the marked image information of the dam area to be detected to the user to provide navigation assistance information.

[0123] The present application provides a device for locating a dam termite nest, and the device for locating a dam termite nest includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the method for locating the dam termite nest in the above-mentioned embodiment one.

[0124] Reference below Figure 7 , which shows a schematic diagram of the structure of a locating device for dam termite nests suitable for implementing the embodiment of the present application. The locating device for dam termite nests in the embodiment of the present application may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 7 The locating device for the dam termite nest shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present application.

[0125] like Figure 7As shown, the locating device for the nest of dam termites may include a processing device 1001 (e.g., a central processing unit, a graphics processor, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM) 1004. Various programs and data required for the operation of the locating device for the nest of dam termites are also stored in the RAM 1004. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems may be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 may allow the locating device for the dam termite nest to communicate wirelessly or wired with other devices to exchange data. Although the locating device for the dam termite nest with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or have instead.

[0126] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application 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. In such an embodiment, the computer program can be downloaded and installed from a network through a communication device, or installed from a storage device 1003, or installed from a ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present application are executed.

[0127] The locating device for termites' nests in dams provided by the present application adopts the locating method for termites' nests in dams in the above-mentioned embodiment, and can solve the technical problem of locating termites' nests in dams. Compared with the prior art, the beneficial effects of the locating device for termites' nests in dams provided by the present application are the same as the beneficial effects of the locating method for termites' nests in dams provided by the above-mentioned embodiment, and the other technical features of the locating device for termites' nests in dams are the same as the features disclosed in the method of the above-mentioned embodiment, and will not be described in detail here.

[0128] It should be understood that the various parts disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0129] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

[0130] The present application provides a computer-readable storage medium having computer-readable program instructions (ie, computer programs) stored thereon, and the computer-readable program instructions are used to execute the method for locating the nest of dam termites in the above-mentioned embodiment.

[0131] The computer-readable storage medium provided in the present application may be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, systems or devices, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this embodiment, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system or device. The program code contained on the computer-readable storage medium may be transmitted using any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0132] The computer-readable storage medium may be included in the locating device for the dam termite nest; or may exist independently without being assembled into the locating device for the dam termite nest.

[0133] The computer-readable storage medium carries one or more programs. When the one or more programs are executed by the locating device for the dam termite nest, the locating device for the dam termite nest can locate the dam termite nest.

[0134] Computer program code for performing the operations of the present application may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate 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 may 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 may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0135] 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 application. 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 alternative implementations, 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.

[0136] The modules involved in the embodiments described in this application may be implemented by software or hardware, wherein the name of the module does not constitute a limitation on the unit itself in some cases.

[0137] The readable storage medium provided in the present application is a computer-readable storage medium, which stores computer-readable program instructions (i.e., computer programs) for executing the above-mentioned method for locating the dam termite nest, and can solve the technical problem of locating the dam termite nest. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided in the present application are the same as the beneficial effects of the method for locating the dam termite nest provided in the above-mentioned embodiment, and will not be described in detail here.

[0138] The present application also provides a computer program product, including a computer program, which implements the steps of the above-mentioned method for locating dam termite nests when executed by a processor.

[0139] The computer program product provided in this application can solve the technical problem of locating termite nests on dams. Compared with the prior art, the beneficial effects of the computer program product provided in this application are the same as the beneficial effects of the method for locating termite nests on dams provided in the above embodiments, which will not be described in detail here.

[0140] The above descriptions are only some embodiments of the present application, and are not intended to limit the patent scope of the present application. All equivalent structural changes made using the contents of the present application specification and drawings under the technical concept of the present application, or direct / indirect applications in other related technical fields are included in the patent protection scope of the present application.

Claims

1. A method for locating termite nests on a dam, characterized in that: The method for locating the dam termite nest comprises: Periodically collect images of several termite sites in the dam area to be inspected; Using the trained termite recognition model to identify termite activities on the plurality of termite site images, so as to determine at least one target camera site, wherein the target camera site is a camera site where termite activities exist; An intersection analysis is performed based on the location information of the target camera site to obtain the location of the target termite nest.

2. The method according to claim 1, characterized in that The performing intersection analysis according to the location information of the target camera station to obtain the location of the target termite nest includes: Extracting coordinate information of the target camera site according to the location information of the target camera site; Generate candidate areas for each target camera station based on the coordinate information; Determine the intersection area of ​​each candidate area and obtain the location of the target termite nest.

3. The method according to claim 2, characterized in that The step of determining the intersection area of ​​each candidate area to obtain the target termite nest location includes: Determine at least one intersection region between the candidate regions to obtain at least one candidate termite nest location; Determining the number of common overlapping regions corresponding to the at least one candidate termite nest location; A credibility analysis is performed on the at least one candidate termite nest location based on the number of common overlapping areas to obtain a target termite nest location.

4. The method according to claim 3, characterized in that Performing a credibility analysis on the at least one candidate termite nest location based on the number of common overlapping regions to obtain a target termite nest location includes: Sorting the candidate termite nest locations based on the number of common overlapping areas; The target termite nest location is determined based on the sorted candidate termite nest locations.

5. The method according to claim 1, characterized in that The plurality of termite site images include at least a first termite site image and a second termite site image continuously captured by the same camera station; The step of identifying termite activities by using a trained termite identification model on the plurality of termite site images to determine at least one termite activity site includes: Determine the location information of the camera site to be detected based on the first termite site image and the second termite site image; The first termite site image and the second termite site image are respectively subjected to termite activity recognition by using a trained termite recognition model to determine whether there is termite activity at the camera site to be detected; At least one termite activity site is generated according to the identification result.

6. The method according to claim 5, characterized in that The step of performing termite activity recognition on the first termite site image and the second termite site image through a trained termite recognition model to determine whether there is termite activity at the camera site to be detected includes: extracting a first image noise residue of the first termite site image and a second image noise residue of the second termite site image respectively; determining a first similarity of a first termite site image according to the first image noise residue and a preset image noise residue; determining a second similarity of the second termite site image according to the second image noise residue and a preset image noise residue; When both the first similarity and the second similarity are smaller than a preset similarity threshold, it is determined that termite activity exists at the camera site to be detected.

7. The method according to any one of claims 1 to 6, characterized in that The method for locating the dam termite nest also includes: Calling a preset program interface to mark the area where the target termite nest is located; The marked image information of the dam area to be inspected is displayed to the user to provide navigation assistance information.

8. A device for locating termite nests on dams, characterized in that: The positioning device of the dam termite nest comprises: A collection module, used for periodically collecting images of several termite sites in the dam area to be detected; An identification module, used to identify termite activities by using a trained termite identification model on the plurality of termite site images to determine at least one target camera site, wherein the target camera site is a camera site where termite activities exist; The analysis module is used to perform intersection analysis based on the location information of the target camera site to obtain the location of the target termite nest.

9. A device for locating termite nests on dams, characterized in that: The locating device for the dam termite nest comprises: a memory, a processor and a locating program for the dam termite nest stored in the memory and executable on the processor, wherein the locating program for the dam termite nest is configured to implement the steps of the locating method for the dam termite nest as described in any one of claims 1 to 7.

10. A storage medium, characterized in that: The storage medium stores a locating program for dam termite nests, and when the locating program for dam termite nests is executed by a processor, the steps of the locating method for dam termite nests according to any one of claims 1 to 7 are implemented.

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