Target detection method and device, computer equipment and readable storage medium

Abstract

The invention relates to a target detection method and device, computer equipment and a readable storage medium. The method is applied to the unmanned aerial vehicle, at least two detector groups are arranged on wings of the unmanned aerial vehicle, each detector group comprises two detectors which are symmetrical relative to the axis of a fuselage of the unmanned aerial vehicle, and in the detection process of a target object, a target detection signal of each target detector in a target detector group is obtained; the target detector group is any one of the at least two detector groups; determining a signal peak-to-peak value time difference corresponding to each target detector according to the target detection signal of each target detector; based on the signal peak-to-peak value time difference corresponding to each target detector and a pre-stored position mapping model, determining position information of the target object; wherein the position mapping model is used for reflecting the corresponding relation between the signal peak-to-peak value time difference corresponding to the target detector and the position information. By adopting the method, the dynamic tracking effect of the target object can be improved.

Description

Technical Field

[0001] The present application relates to the field of detection technologies, and particularly to a target detection method, device, computer device, and readable storage medium. Background Art

[0002] Miniature unmanned aerial vehicles (UAVs) have the characteristics of small size, low cost, and flexible operation, and play an important role in large-scale target search and detection tasks. A magnetic field detector can be carried on the UAV to detect magnetic targets, so as to achieve dynamic tracking of magnetic targets. In traditional technologies, detection signals obtained by the magnetic field detector detecting magnetic targets can be acquired, and then inversion calculations can be performed through these detection signals to obtain the position information of the targets.

[0003] However, the inversion calculation process of deriving position information from detection signals is very complex and requires a large amount of calculation time, resulting in low real-time performance of the UAV's position detection of magnetic targets and poor dynamic tracking effects. Therefore, there is an urgent need for an efficient target detection method. Summary of the Invention

[0004] Based on this, it is necessary to provide a target detection method, device, computer device, and readable storage medium for the above technical problems.

[0005] In a first aspect, the present application provides a target detection method, which is applied to a UAV. At least two detector groups are arranged on the wings of the UAV, each detector group includes two detectors that are symmetric with respect to the fuselage axis of the UAV, and the distances of the detectors in each detector group from the fuselage axis are equal. The method includes:

[0006] During the detection process of a target object, obtaining the target detection signals of each target detector in a target detector group; the target detector group is any detector group among the at least two detector groups;

[0007] According to the target detection signals of each target detector, determining the signal peak-to-peak time difference corresponding to each target detector;

[0008] Based on the signal peak-to-peak time difference corresponding to each target detector and a pre-stored position mapping model, determining the position information of the target object; wherein, the position mapping model is used to reflect the corresponding relationship between the signal peak-to-peak time difference corresponding to the target detector and the position information.

[0009] In one embodiment, the step of obtaining the target detection signals of each target detector in the target detector group during the detection process of the target object includes:

[0010] During the detection process of the target object, each of the detectors detects the target object respectively to obtain the detection signals of the detectors.

[0011] By using the cross-correlation function extremum algorithm, determine the time difference between the first detector and the second detector detecting the target object; wherein, the first detector and the second detector belong to different detector groups and are located on the same side wing.

[0012] According to the distance between the first detector and the second detector and the time difference, calculate the relative speed of the target object in the flight direction of the UAV.

[0013] Based on the relative speed, normalize the detection signals detected by each target detector in the target detector group to obtain the target detection signals.

[0014] In one embodiment, the number of the target detector groups is multiple, and determining the position information of the target object based on the signal peak-to-peak time difference corresponding to each target detector and the pre-stored position mapping model includes:

[0015] For each target detector group, based on the signal peak-to-peak time difference corresponding to the target detectors included in the target detector group and the position mapping model corresponding to the target detector group, determine the candidate position information of the target object.

[0016] According to the candidate position information corresponding to each target detector group, determine the position information of the target object.

[0017] In one embodiment, the method further includes:

[0018] Obtain the sample detection signal corresponding to the target detector; the sample detection signal is determined according to the detection signal obtained by the target detector detecting the sample object.

[0019] For each target detector, according to the signal peak-to-peak time difference of the sample detection signal of the target detector and the position information of the sample object, determine the functional relationship between the signal peak-to-peak time difference and the position information.

[0020] Based on the functional relationship, generate an isoline distribution map of the position information and the signal peak-to-peak time difference.

[0021] Based on each of the isoline distribution maps, generate a position mapping model of the signal peak-to-peak time difference and the position information.

[0022] Second aspect, a target detection device is provided. The device is applied to a drone. At least two detector groups are arranged on the wings of the drone. Each detector group includes two detectors symmetric with respect to the fuselage axis of the drone. The detectors in each detector group are equidistant from the fuselage axis. The device includes:

[0023] A first acquisition module, configured to acquire the target detection signals of each target detector in the target detector group during the detection process of the target object; the target detector group is any detector group among the at least two detector groups;

[0024] A first determination module, configured to determine the signal peak-to-peak time difference corresponding to each target detector according to the target detection signals of each target detector;

[0025] A detection module, configured to determine the position information of the target object based on the signal peak-to-peak time difference corresponding to each target detector and a pre-stored position mapping model; wherein, the position mapping model is used to reflect the corresponding relationship between the signal peak-to-peak time difference corresponding to the target detector and the position information.

[0026] In one embodiment, the first acquisition module is specifically configured to:

[0027] During the detection process of the target object, each detector is used to detect the target object respectively to obtain the detection signals of each detector;

[0028] The time difference between the first detector and the second detector monitoring the target object is determined by the cross-correlation function extremum algorithm; wherein, the first detector and the second detector belong to different detector groups and are located on the same side wing;

[0029] According to the distance between the first detector and the second detector and the time difference, the relative speed of the target object in the flight direction of the drone is calculated;

[0030] Based on the relative speed, the detection signals detected by each target detector in the target detector group are normalized to obtain the target detection signals.

[0031] In one embodiment, the number of the target detector groups is multiple. The detection module is specifically configured to:

[0032] For each target detector group, based on the signal peak-to-peak time difference corresponding to the target detectors included in the target detector group and the position mapping model corresponding to the target detector group, the candidate position information of the target object is determined;

[0033] Determine the position information of the target object according to the candidate position information corresponding to each target detector group.

[0034] In one embodiment, the device further includes:

[0035] A second acquisition module, configured to acquire a sample detection signal corresponding to the target detector; the sample detection signal is determined according to a detection signal obtained by the target detector detecting a sample object;

[0036] A second determination module, configured to, for each target detector, determine a functional relationship between the signal peak-to-peak time difference of the sample detection signal of the target detector and the position information of the sample object;

[0037] A first generation module, configured to generate an isoline distribution map of the position information and the signal peak-to-peak time difference based on the functional relationship;

[0038] A second generation module, configured to generate a position mapping model of the signal peak-to-peak time difference and the position information based on each of the isoline distribution maps.

[0039] In a third aspect, a drone is provided, which includes a fuselage, wings, at least two detector groups, and a processing unit; wherein:

[0040] The at least two detector groups are arranged on the wings, each detector group includes two detectors symmetric with respect to the axis of the fuselage, and the distances of the detectors in each detector group from the axis of the fuselage are equal;

[0041] The processing unit is configured to execute the target detection method according to any one of the first aspects above.

[0042] In one embodiment, the absolute value of the difference between the distance between the two detectors included in each detector group and the length of the wing is less than a first preset threshold;

[0043] The absolute value of the difference between the distance between the two farthest detectors on the same side wing and the width of the wing is less than a second preset threshold.

[0044] In one embodiment, the drone further includes a micro laser pumped magnetometer;

[0045] The micro laser pumped magnetometer is arranged at the front nose position of the drone.

[0046] In a fourth aspect, a computer device is provided, including a memory and a processor, where the memory stores a computer program, and when the processor executes the computer program, the steps of the method according to any one of the first aspects are implemented.

[0047] In a fifth aspect, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method according to any one of the first aspects are implemented.

[0048] The above-mentioned target detection method, device, computer device and readable storage medium can be applied to an unmanned aerial vehicle (UAV). At least two detector groups are arranged on the wings of the UAV, and each detector group includes two detectors that are symmetric with respect to the fuselage axis of the UAV. The detectors in each detector group are equidistant from the fuselage axis. During the detection of the target object, the target detection signals of each target detector in the target detector group can be obtained; the target detector group is any detector group among at least two detector groups. Then, according to the target detection signals of each target detector, the signal peak-to-peak time difference corresponding to each target detector is determined, and further, based on the signal peak-to-peak time difference corresponding to each target detector and a pre-stored position mapping model, the position information of the target object is determined; wherein, the position mapping model is used to reflect the correspondence between the signal peak-to-peak time difference corresponding to the target detector and the position information. In this solution, after obtaining the detection signal of the target detector, the position information of the target object can be directly determined through the position mapping model without performing inversion calculations, improving the detection efficiency and real-time performance of the position information of the target object, thereby improving the dynamic tracking effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] In order to more clearly illustrate the technical solutions in the embodiments of the present application or related technologies, the following will briefly introduce the drawings required for the description of the embodiments of the present application or related technologies. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other related drawings can also be obtained based on these drawings.

[0050] Figure 1 It is a schematic diagram of a UAV in an embodiment;

[0051] Figure 2 It is a schematic flowchart of a target detection method in an embodiment;

[0052] Figure 3 It is a schematic diagram of the signal peak-to-peak time difference in an embodiment;

[0053] Figure 4 It is a schematic diagram of the correspondence between the signal peak-to-peak time difference and the coordinate x and the coordinate z in another embodiment;

[0054] Figure 5 It is a schematic diagram of the coordinates of the detector in another embodiment;

[0055] Figure 6 The contour plot of the coordinates of the sample object passing through the target detector and the signal peak-to-peak time difference in another embodiment;

[0056] Figure 7 The structural block diagram of the target detection device in one embodiment;

[0057] Figure 8 The internal structure diagram of the computer device in one embodiment. Detailed implementation manners

[0058] In order to make the objectives, technical solutions and advantages of the present application clearer and more understandable, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0059] The embodiment of the present application provides a target detection method. This embodiment takes the application of this method to an unmanned aerial vehicle (UAV) as an example for illustration. It can be understood that this method can also be applied to any terminal with data processing functions and flight functions, and the embodiments of the present application do not make any limitations. Among them, the UAV may include a fuselage, wings, and at least two detector groups. Among them: at least two detector groups are arranged on the wings, each detector group includes two detectors symmetric with respect to the axis of the fuselage, and the distances of the detectors in each detector group from the axis of the fuselage are equal. The UAV may further include a micro laser-pumped magnetometer; the micro laser-pumped magnetometer may be arranged at the front nose position of the UAV. Specifically, the absolute value of the difference between the distance between the two detectors included in each detector group and the length of the wing is less than a first preset threshold; the absolute value of the difference between the distance between the two farthest detectors on the same side wing and the width of the wing is less than a second preset threshold. Among them, the first preset threshold and the second preset threshold can be set according to actual needs.

[0060] Refer to Figure 1 , which is a schematic diagram of the UAV provided by the embodiment of the present application. The UAV includes a fuselage 1, wings 2, a detector group 3, a detector group 4, and a micro laser-pumped magnetometer 5. Among them, the detector group 3 includes a detector 31 and a detector 32, and the detector group 4 includes a detector 41 and a detector 42. Each detector is a fluxgate magnetic field detector, and the detector group 3 and the detector group 4 form a triaxial fluxgate magnetic field detector array. The detector 31 and the detector 41 are located on the same side wing; the detector 32 and the detector 42 are located on the other same side wing.

[0061] Each detector is arranged in the front and back directions along the positive direction of the nose of the drone. The distance between detector 31 and detector 41 is d. The distance between detector 32 and detector 42 is also d. The size of the distance d is related to the width of the wing. Within the allowable range of the wing width, d should be as large as possible. Detector 31 and detector 32 are arranged axially symmetrically according to the axis of the drone's fuselage, and the distance is L. Similarly, the distribution of detector 41 and detector 42 is the same. It can be understood that the distance from each group of detectors to the vertical section of the fuselage axis is L / 2. The size of L is related to the length of the wing. L can be equal to or close to the length of a single wing. It can be understood that Figure 1 This is only an example of the structure of the drone, and the number of detector groups can be greater than 2.

[0062] The micro laser-pumped magnetometer installed at the nose position can detect the absolute scalar magnetic field at this position, can provide a standard magnetic field environment for the calibration and compensation of the entire detector array, and also has the function of detecting the scalar magnetic field signal. The fluxgate magnetic field detector used at the wing position can perform three-direction vector detection of the magnetic field. Since the micro laser-pumped magnetometer and the fluxgate magnetic field detector have small volume and weight, they are suitable for the miniaturization requirements of micro and small drone platforms.

[0063] Next, the target detection method provided by this application will be described in detail in combination with specific implementation manners. As Figure 2 shown, the method includes the following steps:

[0064] Step 202, during the detection of the target object, obtain the target detection signals of each target detector in the target detector group.

[0065] Among them, the target detector group is any detector group among at least two detector groups.

[0066] In the embodiment of this application, the drone can detect ground or underground objects by flying, such as for geological exploration, archaeology, or specific target reconnaissance, etc. During the flight of the drone, each detector group and the micro laser-pumped magnetometer are in the on state and perform detection in real time to obtain the target detection signals of each target detector in the target detector group, and then detect the position information of the target object through the target detection signals. The target object can be a magnetic target such as a submarine. Among them, the target detector group can be any detector group among at least two detector groups. The number of target detector groups can be one or more. The selection of the target detector group can be determined according to the actual situation and the detection accuracy requirements, and the embodiment of this application does not make any limitations.

[0067] Step 204, according to the target detection signals of each target detector, determine the peak-to-peak time difference corresponding to each target detector.

[0068] In the embodiments of the present application, during the detection process of the drone, the detection signal of the detector will fluctuate due to the influence of the target object. For each target detector, the drone can determine the signal peak-to-peak time difference corresponding to each target detector according to the target detection signal of the target detector. Among them, the peak-to-peak value (Vpp) refers to the difference between the highest value and the lowest value of the signal within one period, that is, the range between the maximum value and the minimum value. The signal peak-to-peak time difference refers to the absolute value of the difference between the detection time of the highest value of the detection signal and the detection time of the lowest value within one period. As Figure 3 shown, it is a schematic diagram of the signal peak-to-peak time difference.

[0069] Step 206: Determine the position information of the target object based on the signal peak-to-peak time difference corresponding to each target detector and the pre-stored position mapping model.

[0070] Among them, the position mapping model is used to reflect the corresponding relationship between the signal peak-to-peak time difference corresponding to the target detector and the position information. The position mapping model can be pre-constructed according to the detection signals detected by each detector for the sample object.

[0071] In the embodiments of the present application, the position mapping model can be used to reflect the corresponding relationship between the signal peak-to-peak time difference corresponding to the target detector and the position information. For example, the position mapping model can be stored in the form of a data table or other structured data storage methods.

[0072] In one implementation, a coordinate system can be established based on the drone, that is, taking the wing direction as the X-axis direction and the nose direction of the fuselage as the Y-axis to establish a rectangular coordinate system. When the drone is in the detection state and flies forward along the Y-axis direction, the position information of the target object is the relative position between the target object and the drone, which can be specifically expressed as: the coordinates (x, 0, z) when the target object passes through the XZ plane of the coordinate system. For a group of target detectors, which includes two target detectors, two signal peak-to-peak time differences can be obtained. The position mapping model can include the mapping relationship between the two signal peak-to-peak time differences and the coordinate x and the coordinate z. For example, one of the signal peak-to-peak time differences corresponds to the coordinate x, and the other signal peak-to-peak time difference corresponds to the coordinate z. As Figure 4 shown, it is a schematic diagram of the corresponding relationship between the signal peak-to-peak time differences (which can be denoted as TFF1 and TFF2) and the coordinate x and the coordinate z.

[0073] After the UAV obtains the signal peak-to-peak time difference corresponding to each target detector, the signal peak-to-peak time difference can be input into the position mapping model to obtain the coordinates x and z of the target object, so as to obtain the position information of the target object. It can be understood that in the position mapping model, the signal peak-to-peak time difference is a discrete point. If the determined signal peak-to-peak time difference is not the stored signal peak-to-peak time difference, the time difference interval to which the signal peak-to-peak time difference belongs can be determined, and then an interpolation method can be used to determine the coordinates corresponding to the signal peak-to-peak time difference. For example, referring to Figure 4 , if the detected signal peak-to-peak time difference TFF1 is 1.03, the coordinate x is 5, the signal peak-to-peak time difference TFF2 is 1.06, and the coordinate y is 11, the coordinates of the target object are (5, 0, 11). If the signal peak-to-peak time difference TFF1 is 1.1, interpolation calculation is performed based on the numerical range 1.03 to 1.34 and 5 to 7 to obtain the x coordinate corresponding to 1.1. The embodiments of the present application do not limit the interpolation calculation method.

[0074] In this solution, after obtaining the detection signal of the target detector, the position information of the target object can be directly queried through the position mapping model without performing inversion calculation, which improves the detection efficiency and real-time performance of the position information of the target object, thereby improving the dynamic tracking effect.

[0075] Optionally, during the detection of the target object, obtaining the target detection signal of each target detector in the target detector group includes: during the detection of the target object, each detector is used to detect the target object respectively to obtain the detection signal of each detector; the cross-correlation function extreme value algorithm is used to determine the time difference between the first detector and the second detector detecting the target object; wherein, the first detector and the second detector belong to different detector groups and are located on the same side wing; according to the distance and time difference between the first detector and the second detector, calculate the relative speed of the target object in the flight direction of the UAV; based on the relative speed, normalize the detection signals detected by each target detector in the target detector group to obtain the target detection signal.

[0076] In the embodiments of the present application, during the detection process of the target object, the unmanned aerial vehicle (UAV) can detect the target object through each detector respectively to obtain the detection signals of each detector. Then, the time difference between the first detector and the second detector detecting the target object can be determined by the extreme value algorithm of the cross-correlation function. Wherein, the first detector and the second detector belong to different detector groups and are located on the same side wing. For the case where the number of detector groups is greater than 3, the detector group to which the first detector belongs is adjacent to the detector group to which the second detector belongs. Then, the UAV can calculate the ratio of the distance between the first detector and the second detector to the time difference to obtain the relative speed of the target object in the flight direction of the UAV. Furthermore, based on the relative speed, the detection signals detected by each target detector in the target detector group are normalized to obtain the target detection signal.

[0077] Specifically, a coordinate system can be established based on the UAV, that is, taking the wing direction as the X-axis direction and the nose direction of the airframe as the Y-axis to establish a rectangular coordinate system. When the UAV is in the detection state, it flies forward along the Y-axis direction, and the midpoint position at the intersection of the wing and the airframe is the coordinate origin O. According to the right-hand rule, the Z-axis direction is the direction perpendicular to the XY plane and upward through point O. Taking Figure 1 the shown structure as an example, the distance between the two detectors is L, and the distance between two adjacent detectors on the same side wing is d. Then the position coordinates of detector 31 are (L / 2, d / 2, 0), the position coordinates of detector 32 are (-L / 2, d / 2, 0), the position coordinates of detector 41 are (L / 2, -d / 2, 0), and the position coordinates of detector 42 are (-L / 2, -d / 2, 0), as Figure 5 shown. Using , , , to represent the magnetic field vectors (i.e., detection signals) detected by detector 31, detector 32, detector 41, and detector 42 respectively, both are functions of time t.

[0078] Taking the first detector as detector 32 and the second detector as detector 42 as an example, through the extreme value algorithm of the cross-correlation function, the time difference of the target object appearing in detector 32 and detector 42 can be determined . The cross-correlation function can be: , The value of is the value when the cross-correlation function takes the maximum value. Similarly, the time difference of the target object appearing in detector 31 and detector 41 can be determined, which can be denoted as . Generally, = . The relative speed of the target object in the flight direction of the UAV can be denoted as v, then v = d / Among them, d is the distance between detector 32 and detector 42.

[0079] Optionally, in special cases, may be related to are not equal, then the average value of the two can be calculated, and then the distance d between the two detectors is divided by this average value to obtain the relative speed of the target object in the flight direction of the UAV.

[0080] It can be understood that the signal period is inversely proportional to the speed. That is, the higher the speed, the smaller the signal period, and the lower the speed, the larger the signal period. Therefore, the detection signal can be normalized with respect to the speed v to eliminate the influence of speed on the signal characteristics of the detection signal and improve the detection accuracy. Specifically, after determining the relative speed, for each target detector, the peak width of the detection signal detected by the target detector can be determined, and then the peak width is divided by the relative speed to obtain the target detection signal.

[0081] In this solution, the relative speed of the target object relative to the UAV can be determined, and then the detection signal can be normalized with respect to the relative speed v, which can eliminate the influence of speed on the signal characteristics of the detection signal and improve the detection accuracy.

[0082] Optionally, the number of target detector groups is multiple. Based on the signal peak-to-peak time difference corresponding to each target detector and the pre-stored position mapping model, the position information of the target object is determined, including: for each target detector group, based on the signal peak-to-peak time difference corresponding to the target detectors included in the target detector group and the position mapping model corresponding to the target detector group, the candidate position information of the target object is determined; according to the candidate position information corresponding to each target detector group, the position information of the target object is determined.

[0083] In the embodiments of the present application, in order to improve the detection accuracy, the number of target detector groups can be set to multiple. Correspondingly, a position mapping model can be constructed for each target detector group respectively. In this way, for each target detector group, based on the signal peak-to-peak time difference corresponding to the target detectors included in the target detector group and the position mapping model corresponding to the target detector group, the position information of the target object can be determined as the candidate position information. In this way, for each target detector group, a candidate position information can be obtained. Then, according to the candidate position information corresponding to each target detector group, the position information of the target object can be determined. Specifically, the average value can be calculated according to each candidate position information to obtain the position information of the target object, or the center point coordinates corresponding to each candidate position information can be used as the position information of the target object.

[0084] In this solution, the position information of the target object can be determined based on the multiple candidate position information detected by multiple target detector groups, which can avoid the detection errors caused by a single target detector group and improve the accuracy and reliability of target detection.

[0085] Optionally, the embodiment of the present application also provides a process for constructing a position mapping model, which specifically includes: obtaining a sample detection signal corresponding to a target detector; the sample detection signal is determined according to the detection signal obtained by the target detector detecting a sample object; for each target detector, according to the signal peak-to-peak time difference of the sample detection signal of the target detector and the position information of the sample object, determining the functional relationship between the signal peak-to-peak time difference and the position information; based on the functional relationship, generating an isogram distribution map of the position information and the signal peak-to-peak time difference; based on each isogram distribution map, generating a position mapping model of the signal peak-to-peak time difference and the position information.

[0086] In the embodiment of the present application, a sample object can be detected by the target detector of the unmanned aerial vehicle, so as to obtain a sample detection signal. Among them, the sample object can be a magnetic target, and its size or dimension can be determined according to actual detection requirements, which is not limited in the embodiment of the present application. The sample detection signal is determined according to the detection signal obtained by the target detector detecting the sample object, that is, it can be a detection signal normalized based on speed. In one example, during the detection of the sample object by the unmanned aerial vehicle, each detector can detect the sample object respectively to obtain the detection signals of each detector. Then, the cross-correlation function extreme value algorithm can be used to determine the time difference between the first detector and the second detector detecting the sample object; among them, the first detector and the second detector belong to different detector groups and are located on the same side wing. Then, according to the distance between the first detector and the second detector and this time difference, the relative speed of the sample object in the flight direction of the unmanned aerial vehicle is calculated, and then based on this relative speed, the detection signal detected by the target detector is normalized to obtain the sample detection signal. The calculation process of the sample detection signal is similar to that of the target detection signal, which will not be elaborated here.

[0087] For each target detector, data analysis and fitting can be performed according to the signal peak-to-peak time difference of the sample detection signal of the target detector and the position information of the sample object, so as to determine the functional relationship between the signal peak-to-peak time difference and the position information. For example, referring to Figure 5In the coordinate system shown, when the horizontal distance x of the fixed sample object is kept constant and the height z is changed, through data analysis and linear fitting, it can be determined that as the height increases, the peak-to-peak time difference of the signal measured by the target detector approximately linearly increases; when the sample object passes directly above the detector (i.e., x = 0), the peak-to-peak time difference of the signal has a linear relationship with z. When the horizontal distance L between the detectors increases, the linear increasing relationship between the peak-to-peak time difference of the signal and H weakens. Through further analysis of the data, the relationship between the peak-to-peak time difference of the signal and the detection distance R of the sample object (= can be determined, and it can be determined that there is a linear relationship between the two. That is, the peak-to-peak time difference of the signal has a linear functional relationship with the position information. In this way, by performing data analysis on the detection distance and the peak-to-peak time difference of the signal collected at multiple sampling points of the sample object, the specific form of the linear function can be determined.

[0088] Then, based on the functional relationship, an isogram distribution map of the position information and the peak-to-peak time difference of the signal can be generated, so as to obtain the isogram distribution map corresponding to each target detector. Then, based on the isogram distribution maps corresponding to each target detector, a position mapping model of the peak-to-peak time difference of the signal and the position information is generated.

[0089] In this solution, it can be seen from the isogram distribution map that as the detection distance of the sample object increases, the image gradually grows into an ellipse. The image of another target detector is similar. By combining the isogram distribution maps corresponding to the two target detectors in one graph, it can be analyzed that the distribution of these isograms in the two-dimensional plane is directly correlated with the peak-to-peak time difference of the signals of the two target detectors, as Figure 6 shown (some lines are not shown). Among them, X and Z are coordinates, T is the peak-to-peak time difference of the signal, Figure 6 and the value of x in is the absolute value. Therefore, based on the above correlation relationship, a position mapping model is constructed. The position mapping model can reflect the true relationship between the detection distance and the peak-to-peak time difference of the signal, so as to achieve accurate target detection. It can be understood that the position mapping model can be constructed for each target detector group in the above manner, so as to obtain the position mapping model corresponding to each target detector group.

[0090] An embodiment of the present application also provides a drone, which includes a fuselage, wings, at least two detector groups, and a processing unit; wherein:

[0091] The at least two detector groups are arranged on the wings. Each detector group includes two detectors that are symmetric with respect to the axis of the fuselage, and the distances of the detectors in each detector group from the axis of the fuselage are equal;

[0092] The processing unit is used to execute the above-mentioned target detection method.

[0093] Optionally, the absolute value of the difference between the distances between the two detectors included in each of the detector groups and the length of the wing is less than a first preset threshold;

[0094] The absolute value of the difference between the distance between the two detectors on the same side wing and the farthest apart and the width of the wing is less than a second preset threshold.

[0095] Optionally, the drone further includes a micro laser pumped magnetometer;

[0096] The micro laser pumped magnetometer is disposed at the front nose position of the drone.

[0097] It should be understood that although the steps in the flowcharts involved in the above embodiments are displayed in sequence according to the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear description in this article, the execution of these steps has no strict order limit, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above embodiments may include multiple steps or multiple stages. These steps or stages are not necessarily executed at the same moment, but can be executed at different moments. The execution order of these steps or stages is not necessarily sequential, but can be executed alternately or alternately with at least a part of other steps or steps or stages in other steps.

[0098] Based on the same inventive concept, an embodiment of the present application further provides a target detection device for implementing the above-mentioned target detection method. The implementation solutions provided by this device to solve problems are similar to the implementation solutions recorded in the above method. Therefore, the specific limitations in one or more embodiments of the following target detection devices can refer to the limitations on the target detection method in the above text, and will not be repeated here.

[0099] In an exemplary embodiment, as Figure 7 shown, a target detection device is provided. The device is applied to a drone. At least two detector groups are provided on the wings of the drone. Each detector group includes two detectors that are symmetric with respect to the fuselage axis of the drone. The detectors in each detector group are equidistant from the fuselage axis. The device includes: a first acquisition module 710, a first determination module 720, and a detection module 730, where:

[0100] The first acquisition module 710 is configured to acquire the target detection signals of each target detector in the target detector group during the detection process of the target object; the target detector group is any detector group among at least two detector groups;

[0101] The first determination module 720 is configured to determine the signal peak-to-peak time difference corresponding to each target detector according to the target detection signals of each target detector;

[0102] The detection module 730 is configured to determine the position information of the target object based on the signal peak-to-peak time difference corresponding to each target detector and a pre-stored position mapping model; wherein, the position mapping model is used to reflect the correspondence between the signal peak-to-peak time difference corresponding to the target detector and the position information.

[0103] In one embodiment, the first acquisition module 710 is specifically configured to:

[0104] During the detection of the target object, each detector is used to detect the target object respectively to obtain the detection signals of each detector;

[0105] The time difference between the first detector and the second detector for monitoring the target object is determined by the cross-correlation function extremum algorithm; wherein, the first detector and the second detector belong to different detector groups and are located on the same side wing;

[0106] Based on the distance and time difference between the first detector and the second detector, the relative speed of the target object in the flight direction of the UAV is calculated;

[0107] Based on the relative speed, the detection signals detected by each target detector in the target detector group are normalized to obtain the target detection signals.

[0108] In one embodiment, the number of target detector groups is multiple, and the detection module 730 is specifically configured to:

[0109] For each target detector group, based on the signal peak-to-peak time difference corresponding to the target detectors included in the target detector group and the position mapping model corresponding to the target detector group, determine the candidate position information of the target object;

[0110] Based on the candidate position information corresponding to each target detector group, determine the position information of the target object.

[0111] In one embodiment, the device further includes:

[0112] The second acquisition module is configured to acquire the sample detection signals corresponding to the target detectors; the sample detection signals are determined according to the detection signals obtained by the target detectors detecting the sample object;

[0113] The second determination module is configured to, for each target detector, determine the functional relationship between the signal peak-to-peak time difference and the position information according to the signal peak-to-peak time difference of the sample detection signal of the target detector and the position information of the sample object;

[0114] The first generation module is configured to generate an isoline distribution diagram of the position information and the signal peak-to-peak time difference based on a functional relationship.

[0115] The second generation module is configured to generate a position mapping model of the signal peak-to-peak time difference and the position information based on each isoline distribution diagram.

[0116] Each module in the above target detection device can be implemented in whole or in part by software, hardware, and their combination. Each of the above modules can be embedded in the processor of the computer device in hardware form or independent of it, or stored in the memory of the computer device in software form, so that the processor can call and execute the operations corresponding to each of the above modules.

[0117] In an exemplary embodiment, a computer device is provided. The computer device can be a terminal, and its internal structure diagram can be as Figure 8 shown. The computer device includes a processor, a memory, an input / output interface, a communication interface, a display unit, and an input device. Among them, the processor, the memory, and the input / output interface are connected through a system bus, and the communication interface, the display unit, and the input device are connected to the system bus through the input / output interface. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The input / output interface of the computer device is used for exchanging information between the processor and external devices. The communication interface of the computer device is used for communicating with external terminals in a wired or wireless manner, and the wireless manner can be implemented through WIFI, a mobile cellular network, near field communication (NFC), or other technologies. The computer program, when executed by the processor, implements the above method. The display unit of the computer device is used to form a visually visible picture, which can be a display screen, a projection device, or a virtual reality imaging device. The display screen can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, a trackball, or a touchpad provided on the housing of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0118] Those skilled in the art can understand that Figure 8 the structure shown in

[0119] In an exemplary embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, and when the processor executes the computer program, the steps of the above-mentioned target detection method are implemented.

[0120] In an embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the above-mentioned target detection method are implemented.

[0121] In an embodiment, a computer program product is provided, including a computer program. When the computer program is executed by a processor, the steps of the above-mentioned target detection method are implemented.

[0122] It should be noted that the user information (including but not limited to user device identifiers, user personal information, etc.) and data (including but not limited to data for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties. Moreover, the collection, use, and processing of relevant data need to comply with relevant regulations.

[0123] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, database, or other medium used in the embodiments provided in this application can include at least one of non-volatile memory and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc. The databases involved in the embodiments provided in this application can include at least one of relational databases and non-relational databases. Non-relational databases can include distributed databases based on blockchain, etc., without limitation. The processors involved in the embodiments provided in this application can be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, data processing logics based on quantum computing, artificial intelligence (AI) processors, etc., without limitation.

[0124] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope recorded in this application.

[0125] The above-described embodiments merely represent several implementation manners of the present application. The description thereof is relatively specific and detailed, but it should not be construed as a limitation to the patent scope of the present application. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all fall within the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the appended claims.

Claims

1. A target detection method, characterized in that: The method is applied to a drone, wherein at least two detector groups are arranged on the wings of the drone, each of the detector groups comprises two detectors symmetrical with respect to the fuselage axis of the drone, and the detectors in each detector group are at an equal distance from the fuselage axis. The method comprises: In the process of detecting the target object, a target detection signal of each target detector in a target detector group is obtained; the target detector group is any detector group of the at least two detector groups; Determine the peak-to-peak time difference of the signal corresponding to each target detector according to the target detection signal of each target detector; Based on the signal peak-to-peak time difference corresponding to each of the target detectors and a pre-stored position mapping model, the position information of the target object is determined; wherein the position mapping model is used to reflect the correspondence between the signal peak-to-peak time difference corresponding to the target detector and the position information.

2. The method according to claim 1, characterized in that In the process of detecting the target object, obtaining the target detection signal of each target detector in the target detector group includes: During the detection of the target object, the target object is detected by each of the detectors to obtain a detection signal of each of the detectors; Determine the time difference between the first detector and the second detector detecting the target object by using a cross-correlation function extreme value algorithm; wherein the first detector and the second detector belong to different detector groups and are located on the same side of the wing; Calculating a relative speed of the target object in a flight direction of the UAV according to the distance between the first detector and the second detector and the time difference; Based on the relative speed, a detection signal obtained by each target detector in the target detector group is normalized to obtain a target detection signal.

3. The method according to claim 1, characterized in that The number of the target detector groups is multiple, and the determining of the position information of the target object based on the signal peak-to-peak time difference corresponding to each of the target detectors and a pre-stored position mapping model includes: For each target detector group, determining candidate position information of the target object based on a signal peak-to-peak time difference corresponding to the target detectors included in the target detector group and a position mapping model corresponding to the target detector group; The position information of the target object is determined according to the candidate position information corresponding to each target detector group.

4. The method according to claim 1, characterized in that: The method further comprises: Acquire a sample detection signal corresponding to the target detector; the sample detection signal is determined according to a detection signal obtained by the target detector detecting the sample object; For each of the target detectors, determining a functional relationship between the signal peak-to-peak time difference and the position information according to the signal peak-to-peak time difference of the sample detection signal of the target detector and the position information of the sample object; Based on the functional relationship, a contour distribution diagram of the position information and the signal peak-to-peak time difference is generated; Based on each of the contour line distribution diagrams, a position mapping model of the signal peak-to-peak time difference and position information is generated.

5. A target detection device, characterized in that: The device is applied to a drone, wherein at least two detector groups are arranged on the wings of the drone, each of the detector groups comprises two detectors symmetrical with respect to the fuselage axis of the drone, and the detectors in each detector group are at equal distances from the fuselage axis. The device comprises: A first acquisition module is used to acquire a target detection signal of each target detector in a target detector group during the detection process of the target object; the target detector group is any detector group of the at least two detector groups; A first determination module is used to determine the signal peak-to-peak time difference corresponding to each target detector according to the target detection signal of each target detector; A detection module is used to determine the position information of the target object based on the signal peak-to-peak time difference corresponding to each of the target detectors and a pre-stored position mapping model; wherein the position mapping model is used to reflect the correspondence between the signal peak-to-peak time difference corresponding to the target detector and the position information.

6. A drone, characterized in that: The drone comprises a fuselage, wings, at least two detector groups and a processing unit; wherein: The at least two detector groups are arranged on the wing, each detector group includes two detectors symmetrical with respect to the fuselage axis, and the detectors in each detector group are at an equal distance from the fuselage axis; The processing unit is used to execute the target detection method described in any one of claims 1 to 4 above.

7. The drone according to claim 6, characterized in that: The absolute value of the difference between the distance between two detectors included in each detector group and the length of the wing is less than a first preset threshold; An absolute value of a difference between the distance between two detectors on the same side of the wing and the farthest apart from each other and the width of the wing is smaller than a second preset threshold.

8. The drone according to claim 6, characterized in that: The drone also includes a miniature laser-pumped magnetometer; The micro laser pumped magnetometer is arranged at the front nose position of the UAV.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 4 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 4 are implemented.

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