Unmanned aerial vehicle real-time detection system and method based on AI image recognition

Abstract

The invention relates to the technical field of unmanned aerial vehicle detection, in particular to an unmanned aerial vehicle real-time detection system and method based on AI image recognition, and the system comprises an unmanned aerial vehicle system and a ground system. The unmanned aerial vehicle system comprises a flight unit, an information acquisition unit, a data transmission unit and a real-time detection unit; the flight unit comprises a flight power unit and a flight control unit; the information acquisition unit comprises an image information acquisition unit and a space information acquisition unit; the data transmission unit undertakes data transmission between the unmanned aerial vehicle system and the ground system; the real-time detection unit is based on AI image recognition; and the ground system is used for receiving data transmitted back by the data transmission unit on the unmanned aerial vehicle system. According to the invention, the collected plant image is analyzed in real time through the real-time detection unit and is timely fed back to the flight control module to adjust the flight path and the flight attitude of the unmanned aerial vehicle, image information is collected from multiple angles, the data processing load is reduced, and the detection precision can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of UAV detection, and particularly to a real-time UAV detection system and method based on AI image recognition. Background Art

[0002] In the process of agricultural production, effectively achieving the prevention and control of pests and diseases is the prerequisite for ensuring a good harvest of agriculture. If the crop diseases are widely distributed and severely harmful, it will seriously affect the yield and quality of crops. Before the prevention and control of pests and diseases, the first thing to do is to judge the types of pests and diseases. If the judgment of the types of pests and diseases is accurate, the pests and diseases are easy to be cured. If the judgment of the types of pests and diseases is inaccurate, a large amount of manpower and financial resources will be wasted, and the precious treatment time will be delayed.

[0003] Chinese Patent with Publication No. CN 117761077 A discloses a rice pest and disease detection system and method based on a UAV; the rice pest and disease detection system includes a UAV system and a ground control system, and the UAV system includes an image acquisition device, a UAV control device and a communication device; the image acquisition device includes an image acquisition module and an image transmission module; the UAV control device is used to receive the control instructions of the ground control system to adjust the flight state of the UAV and transmit the position information of the UAV back to the ground control system in real time; the ground control system is used to receive the rice images transmitted back by the image transmission module on the UAV and identify the types of rice pests and diseases through a trained rice pest and disease recognition model; the rice pest and disease detection system of the present invention detects the information of rice pests and diseases in the rice field through a UAV, and has higher detection accuracy and detection efficiency.

[0004] However, the above technical solution has the following deficiencies: when the UAV system collects the image information of plants, it can only collect in a relatively general way. If the density of the collected images is large, it is easy to increase the load of system data processing. If the density of the collected images is small, it is easy to miss small-area pest and disease areas, and manual intervention is required to conduct key investigations on the pest and disease areas, so the practicability is poor. Summary of the Invention

[0005] The object of the present invention is to propose a real-time UAV detection system and method based on AI image recognition for the problems existing in the background art.

[0006] The technical solution of the present invention: a real-time UAV detection system based on AI image recognition includes a UAV system and a ground system;

[0007] The UAV system includes a flight unit, an information collection unit, a data transmission unit, and a real-time detection unit;

[0008] The flight unit includes a flight power unit and a flight control unit. The flight power unit provides power for the UAV system, and the flight control unit is used to control the flight attitude of the UAV.

[0009] The information collection unit includes an image information collection unit and a spatial information collection unit. The image information collection unit is used to collect images of plants, and the spatial information collection unit is used to collect spatial information of plants.

[0010] The data transmission unit is responsible for data transmission between the UAV system and the ground system, transmitting the information of the UAV system to the ground system and passing the signals of the ground system to the UAV system.

[0011] The real-time detection unit is based on AI image recognition to perform real-time analysis on the images captured by the information collection unit, identify the types, scopes and locations of plant diseases and pests, and generate the recognition results of plant diseases and pests.

[0012] The ground system is used to receive the data transmitted back by the data transmission unit on the UAV system and send control instructions to the UAV system.

[0013] Preferably, the UAV control unit includes a flight control module and a positioning and navigation module; the flight control module is used to automatically adjust the flight path, flight speed, flight altitude and flight attitude of the UAV, and the positioning and navigation module is used to collect the real-time position information of the UAV in real time and send the real-time position information to the ground system through the data transmission unit.

[0014] Preferably, the image information collection unit includes a pan-tilt and a camera; the pan-tilt is arranged on the UAV to adjust the angle of the camera, and the camera is arranged on the pan-tilt to collect image information of plants.

[0015] Preferably, the data transmission unit is used to transmit the flight status information, captured image information and spatial information of the UAV system to ensure the instant transmission and reception of information.

[0016] Preferably, the real-time detection unit identifies and marks the types of plant diseases and pests, detects the locations where the diseases and pests exist, and generates the recognition results of plant diseases and pests.

[0017] Preferably, the ground system participates in the route planning of the UAV and displays the flight status information, captured image information and spatial information of the UAV system in real time.

[0018] Preferably, the real-time detection unit issues an attitude adjustment instruction to the UAV flight according to the detection results, and the UAV changes its flight trajectory and controls the camera to always point to the location of plant diseases and pests to collect image information from multiple angles.

[0019] On the other hand, the present invention proposes a real-time detection method for drones based on AI image recognition, which uses the above-mentioned real-time detection system for drones based on AI image recognition, and specifically includes the following steps:

[0020] S1. According to the detected range, conduct route planning and setting for the drone. The drone takes off and flies according to the planned route. The flight control module adjusts the flight path, flight speed, flight altitude, and flight attitude in real time according to the condition of the drone;

[0021] S2. During the flight of the drone, the information collection unit works to collect the image information and spatial information of the plants. The real-time detection unit conducts real-time analysis on the collected plant images to preliminarily determine the type of plant diseases and pests;

[0022] S3. When plant diseases and pests are detected, the flight control module adjusts the flight trajectory and flight attitude of the drone and controls the camera to always point to the location of the plant diseases and pests, collects image information from multiple angles, and the positioning and navigation module marks this location;

[0023] S4. The data transmission unit synchronizes the flight data, collected image data, spatial data, etc. of the drone to the ground system in real time;

[0024] S5. The ground system inputs the collected plant images into the AI image recognition model for in-depth analysis to further determine the type of plant diseases and pests. When encountering an undetermined type, it is confirmed through manual judgment and then fed back to the system to help the system continuously learn and optimize.

[0025] Compared with the prior art, the above technical solution of the present invention has the following beneficial technical effects:

[0026] The present invention conducts real-time analysis on the collected plant images through the real-time detection unit, preliminarily determines the type of plant diseases and pests, and timely feeds back to the flight control module to adjust the flight trajectory and flight attitude of the drone, controls the camera to always point to the location of the plant diseases and pests, shortens the photo-taking interval, collects image information from multiple angles, and the positioning and navigation module focuses on marking this location, which can reduce the data processing load while improving the detection accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is the structural framework diagram proposed by the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0028] Example 1, as Figure 1 shown, the real-time detection system for drones based on AI image recognition proposed by the present invention includes a drone system and a ground system;

[0029] The unmanned aerial vehicle (UAV) system includes a flight unit, an information collection unit, a data transmission unit, and a real-time detection unit;

[0030] The flight unit includes a flight power unit and a flight control unit. The flight power unit provides power for the UAV system, and the flight control unit is used to control the flight attitude of the UAV;

[0031] The information collection unit includes an image information collection unit and a spatial information collection unit. The image information collection unit is used to collect images of plants, and the spatial information collection unit is used to collect the spatial information of plants;

[0032] The data transmission unit is responsible for data transmission between the UAV system and the ground system, transmitting the information of the UAV system to the ground system and delivering the signals of the ground system to the UAV system;

[0033] The real-time detection unit is based on AI image recognition and is used to perform real-time analysis on the images captured by the information collection unit, identify the types, scopes, and locations of plant diseases and pests, and generate the recognition results of plant diseases and pests;

[0034] The ground system is used to receive the data transmitted back by the data transmission unit on the UAV system and send control instructions to the UAV system.

[0035] The UAV control unit includes a flight control module and a positioning and navigation module; the flight control module is used to automatically adjust the flight path, flight speed, flight altitude, and flight attitude of the UAV, and the positioning and navigation module is used to collect the real-time position information of the UAV in real time and send the real-time position information to the ground system through the data transmission unit.

[0036] The image information collection unit includes a pan-tilt and a camera; the pan-tilt is installed on the UAV to adjust the angle of the camera, and the camera is installed on the pan-tilt to collect the image information of plants.

[0037] The data transmission unit is used to transmit the flight status information, captured image information, and spatial information of the UAV system to ensure the immediate transmission and reception of information.

[0038] The real-time detection unit identifies and marks the categories of plant diseases and pests, detects the locations where the diseases and pests exist, and generates the recognition results of plant diseases and pests.

[0039] The ground system participates in the route planning of the UAV and displays the flight status information, captured image information, and spatial information of the UAV system in real time.

[0040] The real-time detection unit issues an attitude adjustment instruction to the UAV flight according to the detection results, and the UAV changes its flight trajectory and controls the camera to always point to the location of the plant diseases and pests to collect image information from multiple angles.

[0041] Embodiment 2. An unmanned aerial vehicle (UAV) real-time detection method based on AI image recognition proposed by the present invention adopts the UAV real-time detection system based on AI image recognition in Embodiment 1, and specifically includes the following steps:

[0042] S1. According to the detection range, conduct the route planning and setting of the UAV. The UAV takes off and flies according to the planned route. The flight control module adjusts the flight path, flight speed, flight altitude and flight attitude in real time according to the condition of the UAV;

[0043] S2. During the flight of the UAV, the information acquisition unit works to collect the image information and spatial information of plants. The real-time detection unit analyzes the collected plant images in real time to preliminarily determine the type of plant diseases and pests;

[0044] S3. When plant diseases and pests are detected, the flight control module adjusts the flight trajectory and flight attitude of the UAV and controls the camera to always point to the position of the plant diseases and pests, collects image information from multiple angles, and the positioning and navigation module marks this position;

[0045] S4. The data transmission unit synchronizes the flight data, collected image data and spatial data of the UAV to the ground system in real time;

[0046] S5. The ground system inputs the collected plant images into the AI image recognition model for in-depth analysis to further determine the type of plant diseases and pests. When encountering an undetermined type, it is confirmed through manual judgment and then fed back to the system to help the system continuously learn and optimize.

[0047] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the present invention is not limited thereto. Various changes can be made without departing from the spirit of the present invention within the scope of knowledge possessed by those skilled in the art to which the present invention pertains.

Claims

1. An AI image recognition-based real-time drone detection system, characterized in that, It includes a drone system and a ground system; The drone system includes a flight unit, an information collection unit, a data transmission unit, and a real-time detection unit; The flight unit includes a flight power unit and a flight control unit. The flight power unit provides power for the drone system, and the flight control unit is used to control the flight attitude of the drone; The information collection unit includes an image information collection unit and a spatial information collection unit. The image information collection unit is used to collect images of plants, and the spatial information collection unit is used to collect the spatial information of plants; The data transmission unit is responsible for data transmission between the drone system and the ground system, transmits the information of the drone system to the ground system, and transmits the signals of the ground system to the drone system; The real-time detection unit is based on AI image recognition to perform real-time analysis on the images captured by the information collection unit, identify the types, ranges, and locations of plant diseases and pests, and generate the recognition results of plant diseases and pests; The ground system is used to receive the data transmitted back by the data transmission unit on the drone system and send control instructions to the drone system.

2. The real-time detection system of an unmanned aerial vehicle based on AI image recognition according to claim 1, wherein The drone control unit includes a flight control module and a positioning and navigation module; the flight control module is used to automatically adjust the flight path, flight speed, flight height, and flight attitude of the drone, and the positioning and navigation module is used to collect the real-time position information of the drone in real time and send the real-time position information to the ground system through the data transmission unit.

3. The real-time detection system of an unmanned aerial vehicle based on AI image recognition according to claim 1, characterized in that, The image information collection unit includes a pan-tilt and a camera; the pan-tilt is set on the drone to adjust the angle of the camera, and the camera is set on the pan-tilt to collect the image information of plants.

4. The UAV real-time detection system based on AI image recognition according to claim 1, characterized in that, The data transmission unit is used to transmit the flight status information, captured image information, and spatial information of the drone system to ensure the instant transmission and reception of information.

5. The real-time detection system of an unmanned aerial vehicle based on AI image recognition according to claim 1, characterized in that, The real-time detection unit identifies and marks the types of plant diseases and pests, detects the locations where the diseases and pests exist, and generates the recognition results of plant diseases and pests.

6. The real-time detection system of an unmanned aerial vehicle based on AI image recognition according to claim 5, characterized in that, The ground system participates in the route planning of the drone and displays the flight status information, captured image information, and spatial information of the drone system in real time.

7. The real-time detection system for drones based on AI image recognition according to claim 6, characterized in that, The real-time detection unit issues an attitude adjustment instruction to the drone flight according to the detection results. The drone changes its flight trajectory and controls the camera to always point to the location of the plant diseases and pests, and collects image information from multiple angles.

8. A real-time detection method for drones based on AI image recognition, which uses the real-time detection system for drones based on AI image recognition described in any one of claims 1-7, characterized in that, Specifically, it includes the following steps: S1. According to the detected range, conduct the route planning and setting of the drone. The drone takes off and flies according to the planned route. The flight control module makes real-time adjustments to the flight path, flight speed, flight height, and flight attitude according to the condition of the drone; S2. During the flight of the drone, the information collection unit works to collect the image information and spatial information of plants. The real-time detection unit conducts real-time analysis on the collected plant images and preliminarily determines the types of plant diseases and pests; S3. When plant diseases and pests are detected, the flight control module adjusts the flight trajectory and flight attitude of the drone and controls the camera to always point to the location of the plant diseases and pests, collects image information from multiple angles, and the positioning and navigation module marks this location; S4. The data transmission unit synchronizes the flight data of the UAV, the collected image data, spatial data, etc. to the ground system in real time; S5. The ground system inputs the collected plant images into the AI image recognition model for in-depth analysis to further determine the types of plant pests and diseases. When encountering types that cannot be determined, it is confirmed through manual judgment and then fed back to the system to help the system continuously learn and optimize.

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