Intelligent cruise image analysis system based on unmanned aerial vehicle

Abstract

The invention discloses an intelligent cruise image analysis system based on an unmanned aerial vehicle, relates to the technical field of intelligent cruise, and solves the problems that when an existing unmanned aerial vehicle cruise system carries out human settlement environment image acquisition, the ability to cope with complex human settlement environment conditions is poor, so that the comprehensiveness of acquired data is low, and the data acquisition efficiency is high. And the analysis accuracy of the human settlement environment is relatively low. Comprising the following steps: selecting a cruise route according to route data corresponding to each initial route in planning data, the route data and basic data, and generating a plurality of image acquisition points according to the route data corresponding to the cruise route; controlling the unmanned aerial vehicle to cruise according to a cruise route in the route data; acquiring flight data of the unmanned aerial vehicle in real time, and performing image acquisition to obtain a cruise image; updating the state of the image acquisition point according to the cruise image and uploading the cruise image; generating a cruise report according to the plurality of cruise images; the image of the cruise area can be completely collected, and the accuracy of human settlement environment analysis is improved.

Description

Technical Field

[0001] The present application belongs to the field of intelligent cruise technology and relates to intelligent cruise technology, specifically to an intelligent cruise image analysis system based on a drone. Background Art

[0002] Drone intelligent cruise is an automated aerial inspection solution that integrates advanced flight control, precise positioning, sensor integration and intelligent data processing technology. It can perform inspection tasks efficiently, accurately and safely in multiple fields such as urban traffic management, environmental monitoring, public security and emergency response, transmit images and data in real time, and provide timely and accurate information support for decision-making, showing strong application value and broad development prospects.

[0003] When current drones detect human settlements, they usually manually control the drone to fly to a specific location to take photos of the residential area, or set a route, set fixed collection points on the route, automatically control the drone to take photos of the residential area on the route, and then analyze the photos to obtain the detection results of the human settlement environment; no matter which method is used, the driver who controls the drone needs to have sufficient understanding of the detection environment to ensure that the collected images can fully reflect the residential area; in rural environments, since people's residential areas are relatively scattered and the floor heights of each household are different, the above method is difficult to adapt to the complex rural environment for image collection of the human settlement environment, resulting in low comprehensiveness of the collected data, and thus low accuracy in the analysis of the human settlement environment. Therefore, a drone-based intelligent cruise image analysis system is needed. Summary of the invention

[0004] The present application aims to solve at least one of the technical problems existing in the prior art; to this end, the present application proposes an image analysis system based on an intelligent cruise of a drone, which is used to solve the technical problem that the existing drone cruise system has poor ability to cope with the complex conditions of the human living environment when collecting images of the human living environment, resulting in low comprehensiveness of the collected data, and thus low accuracy in the analysis of the human living environment.

[0005] To achieve the above-mentioned purpose, the first aspect of the present application provides an image analysis system based on an intelligent cruise of a UAV, comprising: an interaction module, an acquisition planning module, a cruise control module, an image acquisition module, an image analysis module and a database;

[0006] The interactive module: obtains basic data and planning data of the cruise area, and displays the cruise report;

[0007] The acquisition planning module extracts route data corresponding to each initial route in the planning data, selects a cruise route according to each route data and basic data, and generates a number of image acquisition points according to the route data corresponding to the cruise route;

[0008] The cruise control module controls the UAV to cruise according to the cruise route in the route data;

[0009] The image acquisition module: acquires the flight data of the UAV in real time, acquires the cruise image according to the flight data and the corresponding image acquisition points; updates the image acquisition point status according to the cruise image; and uploads the cruise image according to the acquisition point status;

[0010] The image analysis module is used to obtain a number of cruise images and generate a cruise report based on the cruise images.

[0011] The present application obtains basic data and planning data of the cruise area, selects the most suitable route as the cruise route according to the route data and basic data corresponding to each initial route in the planning data, and adaptively examines the image acquisition points according to the route data and the basic data of the cruise area, and controls the UAV to cruise according to the cruise route in the route data; obtains the flight data of the UAV in real time, and acquires cruise images according to the flight data and the corresponding image acquisition points; updates the status of the image acquisition points according to the cruise images; uploads the cruise images according to the status of the acquisition points; obtains several cruise images, and generates a cruise report according to the several cruise images; selects the optimal cruise route according to the characteristics of the cruise area, and generates image acquisition points that meet the cruise area, so that the image of the cruise area can be acquired as completely as possible, so as to increase the accuracy of the analysis of the human living environment in the cruise area.

[0012] Preferably, the selecting of the cruise route according to each route data and basic data includes:

[0013] The cruise area is rasterized to obtain a number of cruise grids, and the available airspace height in each cruise grid in the basic data is extracted;

[0014] Extract the initial route from the route data, and number each cruise grid according to the initial route; extract the flight altitude within each cruise area from the route data;

[0015] Generate a route score corresponding to the initial route according to the flight altitude and available airspace altitude in each cruise grid;

[0016] An initial route with a high route score is selected as the cruise route, and the route data corresponding to the initial route is used as the cruise route data.

[0017] Preferably, generating a route score corresponding to the initial route according to the flight altitude and the available airspace altitude in each cruise grid includes:

[0018] Get the number i of each cruise grid; set the available airspace height corresponding to the cruise grid to KHi; set the flight altitude corresponding to the cruise grid to FHin; n is the number of the initial route;

[0019] By formula The route score HPn of the initial route numbered n is calculated; where β1 and β2 are proportional coefficients; DH is the unit height; ε i is the optimal height adjustment coefficient corresponding to the grid area numbered i, FH0=0.

[0020] Preferably, the generating of a plurality of image acquisition points according to the route data corresponding to the cruise route includes:

[0021] Obtain the flight altitude FHi and the flight track length FCi in each cruise grid in the cruise route data corresponding to the cruise route;

[0022] By formula The number of acquisition points TS is calculated; wherein BH is the standard acquisition height, and BC is the distance between the corresponding image acquisition points at the standard acquisition height;

[0023] An equal number of image acquisition points are set on the cruise route within the corresponding cruise grid; the number of acquisition points corresponding to each cruise grid is calculated in turn, and the image acquisition points of each cruise grid are set according to the number of acquisition points.

[0024] Preferably, updating the image acquisition point status according to the cruise image includes:

[0025] Acquire a cruise image, and input the cruise image into a clarity detection model to obtain a clarity score of the cruise image;

[0026] Determine whether the clarity score is greater than a set clarity score threshold; if so, set the image acquisition point state of the image acquisition point corresponding to the cruise image to the acquired state; if not, set the image acquisition point state of the image acquisition point corresponding to the cruise image to the unqualified state;

[0027] The collection point status includes a waiting state, a collected state and an unqualified state.

[0028] Preferably, the clarity detection model is obtained by training an artificial intelligence model, including:

[0029] Obtain a number of cruise images and their corresponding clarity scores through a database; integrate the cruise images and clarity scores into a number of training data and test data;

[0030] The artificial intelligence model is trained using the training data; the trained artificial intelligence model is tested using the test data; and finally a clarity detection model is obtained, which inputs the cruise image and outputs the corresponding clarity score; wherein the artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0031] Preferably, uploading the cruise image according to the state of the collection point includes:

[0032] When the state of the acquisition point is the acquired state, the flight altitude of the image acquisition point is obtained and marked as the acquisition altitude, and the cruise image and acquisition altitude corresponding to the image acquisition point are packaged and uploaded to the image analysis module.

[0033] Preferably, generating a cruise report according to a plurality of cruise images comprises:

[0034] Obtain a cruise image, pre-process the cruise image to obtain a test image; input the test image into a debris recognition model to obtain a number of debris labels and corresponding debris coordinates;

[0035] An environmental score will be generated based on the debris label and debris coordinates;

[0036] The environmental score and the corresponding cruise image are integrated into a collection point analysis result data packet of the image collection point; the collection point analysis result data packet is integrated into a cruise report according to the order of the cruise route; and the debris recognition model is obtained through artificial intelligence model training.

[0037] Preferably, the preprocessing of the cruise image to obtain the inspection image includes:

[0038] Obtain the cruise image and its corresponding acquisition altitude; set the magnification ratio according to the acquisition altitude. The higher the flight altitude, the larger the magnification ratio is set; the cropping size is the size of the image input into the debris recognition model; enlarge the cruise image according to the magnification ratio, and then crop it to obtain the inspection image.

[0039] Preferably, the debris recognition model is obtained by training an artificial intelligence model, including:

[0040] Obtain a number of inspection images, and a number of debris labels and debris coordinates corresponding thereto through a database; integrate the inspection images, and a number of debris labels and debris coordinates corresponding thereto into a number of training data and inspection data;

[0041] The artificial intelligence model is trained using training data, and the trained artificial intelligence model is tested using test data; ultimately, a debris recognition model is obtained, whose input is a test image and whose output is a number of debris labels and debris coordinates; wherein the artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0042] Preferably, the generating of the environment score according to the debris label and the debris coordinates includes:

[0043] For the debris coordinates corresponding to several debris labels identified in the inspection image, cluster analysis is performed on each debris coordinate to obtain several clusters, and the cluster density PDMm of each cluster is obtained; m is the number of the cluster, and the number of debris labels and the number of types of debris labels in each cluster are obtained, which are marked as ZWSm and ZZSm respectively;

[0044] By formula The environmental score JP of the inspection image is calculated, wherein MP is the full score of the set environmental score; ZDM is the set standard clustering density; α1 and α2 are proportional coefficients.

[0045] Compared with the prior art, the beneficial effects of this application are:

[0046] 1. The present application obtains basic data and planning data of the cruise area, selects the most suitable route as the cruise route according to the route data and basic data corresponding to each initial route in the planning data, and adaptively examines the image acquisition points according to the route data and the basic data of the cruise area, and controls the UAV to cruise according to the cruise route in the route data; obtains the flight data of the UAV in real time, and acquires cruise images according to the flight data and the corresponding image acquisition points; updates the image acquisition point status according to the cruise image; uploads the cruise image according to the acquisition point status; acquires several cruise images, and generates a cruise report according to the several cruise images; selects the optimal cruise route according to the characteristics of the cruise area, and generates image acquisition points that meet the cruise area, so that the image of the cruise area can be acquired as completely as possible, so as to increase the accuracy of the analysis of the human living environment in the cruise area.

[0047] 2. This embodiment selects the most suitable route as the current cruise route by considering the matching between each set route and the specific cruise area, so that the final cruise effect is better and can better reflect the human living environment characteristics of the cruise area. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] 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, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 This is a module diagram of the intelligent cruise image analysis system in this application;

[0050] Figure 2 Schematic diagram of the execution flow of the intelligent cruise image analysis system in this application. DETAILED DESCRIPTION

[0051] The technical solution of the present application will be described clearly and completely in conjunction with the embodiments below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0052] See also Figure 1-Figure 2 , the first aspect of the present application provides an intelligent cruise image analysis system based on a drone, including: an interaction module, an acquisition planning module, a cruise control module, an image acquisition module, an image analysis module and a database;

[0053] Interactive module: The interactive module includes a display unit and an interactive input unit; the interactive input unit is used to obtain basic data and planning data of the cruise area, and the display unit is used to display the cruise report; the cruise area is the area where the drone will cruise, the basic data is the height of several buildings or non-buildings in the cruise area, and the planning data is several routes set by the staff to cruise the cruise area, as well as route-related data; including several initial routes and their corresponding route data, the initial route is the route set by the staff; the route data is the flight speed and flight altitude of each point on the route corresponding to the initial route;

[0054] Acquisition planning module: extract the route data corresponding to each initial route in the planning data, select the cruise route according to each route data and basic data, and generate several image acquisition points according to the route data corresponding to the cruise route; the cruise route is the flight route executed by the final UAV, and the image acquisition point is the position where the UAV needs to hover for image acquisition;

[0055] Cruise control module: controls the UAV to cruise according to the cruise route in the route data;

[0056] Image acquisition module: real-time acquisition of UAV flight data, flight data including real-time position, flight speed and angle of the UAV; image acquisition is performed according to flight data and corresponding image acquisition points to obtain cruise images; image acquisition point status is updated according to cruise images; acquisition point status includes pending acquisition status, acquired status and unqualified status; pending acquisition status means that the UAV has not passed the image acquisition point and has not performed image acquisition at the image acquisition point, which is the initial state of the image acquisition point; acquired status means that the image acquisition point has performed image acquisition and the acquired image meets the requirements; unqualified status means that the image acquisition point has performed image acquisition and the acquired image does not meet the requirements; upload the cruise image according to the acquisition point status; specifically, when the real-time position of the UAV is the same as the position of the image acquisition point, image acquisition is performed to determine whether the image clarity of the acquired image is qualified. If qualified, the acquisition point status corresponding to the image acquisition point is modified to the acquired status; otherwise, the acquisition point status corresponding to the image acquisition point is modified to the unqualified status; if the acquisition point status of the image acquisition point is qualified, the acquired image is uploaded, otherwise, it is not uploaded;

[0057] Through the above process, this embodiment collects and marks unqualified collected image points during the cruise, and re-collects them during the next cruise, thereby ensuring the speed of this cruise;

[0058] The image analysis module acquires a number of cruise images and generates a cruise report based on the cruise images; the cruise report includes images collected at each image collection point and corresponding environmental scores.

[0059] This embodiment obtains basic data and planning data of the cruise area, selects the most suitable route as the cruise route according to the route data and basic data corresponding to each initial route in the planning data, and adaptively examines the image acquisition points according to the route data and the basic data of the cruise area, and controls the drone to cruise according to the cruise route in the route data; obtains the flight data of the drone in real time, and acquires cruise images according to the flight data and the corresponding image acquisition points; updates the status of the image acquisition points according to the cruise images; uploads the cruise images according to the status of the acquisition points; obtains several cruise images, and generates a cruise report according to the several cruise images; selects the optimal cruise route according to the characteristics of the cruise area, and generates image acquisition points that meet the cruise area, so that the image of the cruise area can be acquired as completely as possible, so as to increase the accuracy of the analysis of the human living environment in the cruise area.

[0060] The cruise route is selected according to each route data and basic data, including: rasterizing the cruise area to obtain a number of cruise grids. There are many methods for rasterization. In this embodiment, the length and width of the grid are set in advance by the staff, and then the cruise area is rasterized; the available airspace height in each cruise grid in the basic data is extracted; the available airspace height is the height at which the drone can fly in the corresponding cruise grid; specifically, if the height of the corresponding residential building in the grid area is 8 meters, the available airspace height of the drone is 8 meters, and the flight height of the drone is at least greater than 8 meters;

[0061] Extract the initial route from the route data, and number each cruise grid according to the initial route; that is, number each cruise grid according to the order of grids that the UAV passes through in the initial route, the first cruise grid passed is numbered 1; the second cruise grid is numbered 2; extract the flight altitude in each cruise area in the route data;

[0062] Generate a route score corresponding to the initial route according to the flight altitude and available airspace altitude in each cruise grid;

[0063] An initial route with a high route score is selected as the cruise route, and the route data corresponding to the initial route is used as the cruise route data.

[0064] This embodiment selects the most suitable route as the current cruise route by considering the matching between each set route and the specific cruise area, so that the final cruise effect is better and can better reflect the specific environmental characteristics of the cruise area.

[0065] Generate a route score corresponding to the initial route according to the flight altitude and the available airspace altitude in each cruise grid, including: obtaining the number i of each cruise grid; setting the available airspace altitude corresponding to the cruise grid to KHi; setting the flight altitude corresponding to the cruise grid to FHin; n is the number of the initial route;

[0066] By formula The route score HPn of the initial route numbered n is calculated; where β1 and β2 are proportional coefficients; they are used to adjust the flight altitude within the cruise grid and the ratio of the flight altitude difference between adjacent cruise grids to the route score of the initial route. The specific value is set based on experience; DH is the unit height, which is used to remove the parameter unit; ε iIt is the optimal height adjustment coefficient corresponding to the grid area numbered i, which is used to adjust the available airspace height to the optimal acquisition height. It is set according to the height distribution of buildings in the cruise grid. When the height of buildings in the cruise grid is concentrated close to the available airspace height, the higher the altitude you need to fly to capture the ground, the larger the optimal height adjustment coefficient is set. The specific value is set according to expert experience; FH0 = 0.

[0067] In this embodiment, the route score of each initial route is calculated by the above formula; when the flight altitude of the initial route in each cruise grid is closer to the optimal acquisition altitude, the image effect acquired in the corresponding cruise grid using the route is better, and the corresponding route score is set higher; when the flight altitude difference in each cruise grid is large, when the image analysis is performed later, the proportion of objects in the image is too different, and the reference between the images acquired by adjacent cruise grids is weak; the more unreasonable the initial route planning is, the lower the corresponding route score is.

[0068] Generating a number of image acquisition points according to the route data corresponding to the cruise route, including: obtaining the flight altitude FHi and the flight track length FCi in each cruise grid in the cruise route data corresponding to the cruise route;

[0069] By formula The number of acquisition points TS is calculated; wherein BH is the standard acquisition height, and BC is the distance between the corresponding image acquisition points at the standard acquisition height;

[0070] An equal number of image acquisition points are set on the cruise route within the corresponding cruise grid; it can be understood that the intercepted flight trajectory within each cruise grid is approximately a straight line; the number of acquisition points corresponding to each cruise grid is calculated in turn, and the image acquisition points of each cruise grid are set according to the number of acquisition points.

[0071] In this embodiment, the number of collection points in each cruise grid is calculated by the above formula. When the flight altitude in the cruise grid is higher, the range of the ground area included in the image taken by the drone is larger. By increasing the distance between the collection points, the content repetition of the images collected by adjacent collection points is avoided to be too high.

[0072] Updating the state of the image acquisition point according to the cruise image includes: acquiring the cruise image, inputting the cruise image into a clarity detection model to obtain a clarity score of the cruise image;

[0073] Determine whether the clarity score is greater than a set clarity score threshold; if so, set the image acquisition point status of the image acquisition point corresponding to the cruise image to the acquired status; if not, set the image acquisition point status of the image acquisition point corresponding to the cruise image to the unqualified status; the acquisition point status includes a to-be-acquired status, an acquired status, and an unqualified status.

[0074] The clarity detection model is obtained through artificial intelligence model training, including: obtaining a number of cruise images and their corresponding clarity scores through a database; the clarity scores are experts' clarity scores based on the cruise images; integrating the cruise images and clarity scores into a number of training data and test data;

[0075] The artificial intelligence model is trained using the training data; the trained artificial intelligence model is tested using the test data; specifically, the cruise image in the test data is input into the trained artificial intelligence model to obtain an output clarity score, and it is determined whether the difference between the output clarity score and the clarity score recorded in the test data is within an acceptable range; if so, it means that the test data has passed the test, and the next set of test data is tested; if not, it means that the set of data has not passed the test, and the relevant parameters of the artificial intelligence are adjusted, and the set of test data is continued to be used for testing; until a set proportion of the test data passes the test, a clarity detection model is finally obtained with the cruise image as input and the corresponding clarity score as output; wherein the artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0076] This embodiment updates the state of the collection points in the above manner to ensure that there will be no omission or repeated collection of collection points.

[0077] The cruise image is uploaded according to the state of the acquisition point, including: when the state of the acquisition point is the acquired state, the flight altitude of the image acquisition point is obtained and marked as the acquisition altitude, and the cruise image and the acquisition altitude corresponding to the image acquisition point are packaged and uploaded to the image analysis module; that is, when the acquired image meets the upload requirements, it is uploaded.

[0078] Generate a cruise report according to a number of cruise images, including: obtain cruise images, pre-process the cruise images to obtain inspection images; specifically: pre-process the cruise images to obtain inspection images, including: obtain cruise images and their corresponding acquisition heights; set the magnification ratio according to the acquisition height, when the flight altitude is higher, the magnification ratio is set to be larger; the cropping size is the size of the image input into the debris recognition model; magnify the cruise image according to the magnification ratio, and then crop it to obtain the inspection image; perform operations such as cropping and magnifying the image according to the acquisition data, and unify the size and ratio of images collected at different heights; facilitate model analysis; input the inspection image into the debris recognition model to obtain a number of debris labels and corresponding debris coordinates;

[0079] An environmental score will be generated based on the debris label and debris coordinates;

[0080] The environmental score and the corresponding cruise image are integrated into a collection point analysis result data packet of the image collection point; the collection point analysis result data packet is integrated into a cruise report according to the order of the cruise route; and the debris recognition model is obtained through artificial intelligence model training.

[0081] The debris recognition model is obtained through artificial intelligence model training, including: obtaining a number of inspection images, and a number of debris labels and debris coordinates corresponding thereto through a database, wherein the debris labels are type labels of debris in the inspection images, including garbage and firewood piles, etc., and the debris coordinates are the center coordinates of the recognition frame corresponding to the debris labels; integrating the inspection images, and the number of debris labels and debris coordinates corresponding thereto into a number of training data and inspection data;

[0082] The artificial intelligence model is trained using the training data, and the trained artificial intelligence model is tested using the test data; specifically, the test image in the test data is input into the trained artificial intelligence model to obtain the output debris labels and debris coordinates, and it is determined whether the number and type of the output debris labels are the same as the number and type of the debris labels in the test data, and whether the Euclidean distance between each output debris label and the corresponding debris coordinates in the test data is within an acceptable range; if yes, it means that the group of test data has passed the test, and the next group of test data is tested; if not, the relevant parameters of the artificial intelligence model are adjusted, and the test of the current group of test data is continued; until a set proportion of the test data passes the test, finally a debris recognition model with the test image as input and a number of debris labels and debris coordinates as output is obtained; wherein the artificial intelligence model includes a BP neural network model and an RBF neural network model.

[0083] An environmental score is generated according to the debris labels and debris coordinates, including: performing cluster analysis on the debris coordinates corresponding to the several debris labels identified in the inspection image to obtain several clusters, and obtaining the cluster density PDMm of each cluster. In this embodiment, the cluster density may be the average value of the point density of each cluster point in the corresponding cluster; m is the number of the cluster, and the number of debris labels and the number of types of debris labels in each cluster are obtained, which are marked as ZWSm and ZZSm respectively;

[0084] By formula The environmental score JP of the inspection image is calculated, wherein MP is the full score of the set environmental score; ZDM is the set standard clustering density; α1 and α2 are proportional coefficients, which are used to adjust the proportion of the number of debris labels and the number of types of debris labels on the environmental score, and the specific values ​​are set according to expert experience.

[0085] This embodiment calculates the environmental score corresponding to the inspection image through the above formula. When the types and numbers of debris identified in the inspection image are greater, it means that the environment of the area corresponding to the inspection image is poor, and the corresponding environmental score is set lower; when the number of clusters is greater and the density of each cluster is smaller, it means that the distribution of debris in the inspection image is more dispersed, which means that the environment of the area corresponding to the inspection image is more cluttered, and the corresponding environmental score is set lower.

[0086] Part of the data in the above formula is calculated by removing the dimension and taking its numerical value. The formula is a formula closest to the actual situation obtained by software simulation of a large amount of collected data; the preset parameters and preset thresholds in the formula are set by technical personnel in this field according to actual conditions or obtained through simulation of a large amount of data.

[0087] How this application works:

[0088] The present application obtains basic data and planning data of the cruise area, selects the most suitable route as the cruise route according to the route data and basic data corresponding to each initial route in the planning data, and adaptively examines the image acquisition points according to the route data and the basic data of the cruise area, and controls the UAV to cruise according to the cruise route in the route data; obtains the flight data of the UAV in real time, and acquires cruise images according to the flight data and the corresponding image acquisition points; updates the status of the image acquisition points according to the cruise images; uploads the cruise images according to the status of the acquisition points; obtains several cruise images, and generates a cruise report according to the several cruise images; selects the optimal cruise route according to the characteristics of the cruise area, and generates image acquisition points that meet the cruise area, so that the image of the cruise area can be acquired as completely as possible, so as to increase the accuracy of the analysis of the human living environment in the cruise area.

[0089] The above embodiments are only used to illustrate the technical method of the present application and are not intended to limit it. Although the present application has been described in detail with reference to the preferred embodiments, a person of ordinary skill in the art should understand that the technical method of the present application may be modified or replaced by equivalents without departing from the spirit and scope of the technical method of the present application.

Claims

1. Based on the UAV intelligent cruise image analysis system, including: Interaction module, acquisition planning module, cruise control module, image acquisition module, image analysis module and database; characterized in that, The interactive module: obtains basic data and planning data of the cruise area, and displays the cruise report; The acquisition planning module extracts route data corresponding to each initial route in the planning data, selects a cruise route according to each route data and basic data, and generates a number of image acquisition points according to the route data corresponding to the cruise route; The cruise control module controls the UAV to cruise according to the cruise route in the route data; The image acquisition module: acquires the flight data of the UAV in real time, acquires the cruise image according to the flight data and the corresponding image acquisition points; updates the image acquisition point status according to the cruise image; and uploads the cruise image according to the acquisition point status; The image analysis module is used to obtain a number of cruise images and generate a cruise report based on the cruise images.

2. The UAV-based intelligent cruise image analysis system according to claim 1 is characterized in that: The cruise route is selected according to various route data and basic data, including: The cruise area is rasterized to obtain a number of cruise grids, and the available airspace height in each cruise grid in the basic data is extracted; Extract the initial route from the route data, and number each cruise grid according to the initial route; extract the flight altitude within each cruise area from the route data; Generate a route score corresponding to the initial route according to the flight altitude and available airspace altitude in each cruise grid; An initial route with a high route score is selected as the cruise route, and the route data corresponding to the initial route is used as the cruise route data.

3. The UAV-based intelligent cruise image analysis system according to claim 2 is characterized in that: The generating of the route score corresponding to the initial route according to the flight altitude and the available airspace altitude in each cruising grid includes: Get the number i of each cruise grid; set the available airspace height corresponding to the cruise grid to KHi; set the flight altitude corresponding to the cruise grid to FHin; n is the number of the initial route; By formula The route score HPn of the initial route numbered n is calculated; where β1 and β2 are proportional coefficients; DH is the unit height; ε i is the optimal height adjustment coefficient corresponding to the grid area numbered i; FH0=0.

4. The UAV-based intelligent cruise image analysis system according to claim 3 is characterized in that: The generating of a plurality of image acquisition points according to the route data corresponding to the cruise route includes: Obtain the flight altitude FHi and the flight track length FCi in each cruise grid in the cruise route data corresponding to the cruise route; By formula The number of acquisition points TS is calculated; wherein BH is the standard acquisition height, and BC is the distance between the corresponding image acquisition points at the standard acquisition height; An equal number of image acquisition points are set on the cruise route within the corresponding cruise grid; the number of acquisition points corresponding to each cruise grid is calculated in turn, and the image acquisition points of each cruise grid are set according to the number of acquisition points.

5. The UAV-based intelligent cruise image analysis system according to claim 1 is characterized in that: The updating of the image acquisition point status according to the cruise image comprises: Acquire a cruise image, and input the cruise image into a clarity detection model to obtain a clarity score of the cruise image; Determine whether the clarity score is greater than a set clarity score threshold; if so, set the image acquisition point state of the image acquisition point corresponding to the cruise image to the acquired state; if not, set the image acquisition point state of the image acquisition point corresponding to the cruise image to the unqualified state; The collection point status includes a waiting state, a collected state and an unqualified state.

6. The UAV-based intelligent cruise image analysis system according to claim 5 is characterized in that: The clarity detection model is obtained by training an artificial intelligence model, including: Obtain a number of cruise images and their corresponding clarity scores through a database; integrate the cruise images and clarity scores into a number of training data and test data; The artificial intelligence model is trained using the training data; the trained artificial intelligence model is tested using the test data; and finally a clarity detection model is obtained whose input is the cruise image and whose output is the corresponding clarity score.

7. The UAV-based intelligent cruise image analysis system according to claim 1 is characterized in that: The uploading of the cruise image according to the state of the acquisition point includes: When the state of the acquisition point is the acquired state, the flight altitude of the image acquisition point is obtained and marked as the acquisition altitude, and the cruise image and acquisition altitude corresponding to the image acquisition point are packaged and uploaded to the image analysis module.

8. The UAV-based intelligent cruise image analysis system according to claim 1 is characterized in that: The generating of the cruise report according to the plurality of cruise images comprises: Obtain a cruise image, pre-process the cruise image to obtain a test image; input the test image into a debris recognition model to obtain a number of debris labels and corresponding debris coordinates; An environmental score will be generated based on the debris label and debris coordinates; The environmental score and the corresponding cruise image are integrated into a collection point analysis result data packet of the image collection point; the collection point analysis result data packet is integrated into a cruise report according to the order of the cruise route; and the debris recognition model is obtained through artificial intelligence model training.

9. The UAV-based intelligent cruise image analysis system according to claim 8 is characterized in that: The debris recognition model is obtained through artificial intelligence model training, including: Obtain a number of inspection images, and a number of debris labels and debris coordinates corresponding thereto through a database; integrate the inspection images, and a number of debris labels and debris coordinates corresponding thereto into a number of training data and inspection data; The artificial intelligence model is trained using the training data, and the trained artificial intelligence model is tested using the test data; ultimately, a debris recognition model is obtained whose input is the test image and whose output is a number of debris labels and debris coordinates.

10. The UAV-based intelligent cruise image analysis system according to claim 8, characterized in that: The environment score is generated according to the debris labels and debris coordinates, including: For the debris coordinates corresponding to several debris labels identified in the inspection image, cluster analysis is performed on each debris coordinate to obtain several clusters, and the cluster density PDMm of each cluster is obtained; m is the number of the cluster, and the number of debris labels and the number of types of debris labels in each cluster are obtained, which are marked as ZWSm and ZZSm respectively; By formula The environmental score JP of the inspection image is calculated, wherein MP is the full score of the set environmental score; ZDM is the set standard clustering density; α1 and α2 are proportional coefficients.

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