Unmanned aerial vehicle intelligent tracking control system based on artificial intelligence image recognition
By integrating artificial intelligence image recognition technology in the drone system, using deep learning algorithms to identify target objects, and combining adaptive flight control algorithms, the drone accurately tracks target objects, solving the problems of low tracking accuracy and susceptibility to interference in traditional systems.
Patent Information
- Application Number
- CN202510245846.X
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-03-04
- Publication Date
- 2025-05-30
- Estimated Expiration
- Not applicable · inactive patent
AI Technical Summary
Traditional drone tracking and control systems rely on external devices such as radar and GPS, resulting in low tracking accuracy and easy interference, making it difficult to accurately identify and track target objects.
Using image recognition technology based on artificial intelligence, the image data of the target object is obtained through the image acquisition unit. The image recognition unit uses deep learning algorithms (such as convolutional neural networks or recurrent neural networks) to extract and recognize features, outputs position information of the target object, and the flight control unit adjusts the flight attitude and trajectory of the drone based on the position information to achieve accurate tracking of the target object.
It improves the accuracy and stability of drone tracking, reduces dependence on external devices, and achieves accurate identification and tracking of target objects.
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of unmanned aerial vehicle (UAV) control, and particularly to an intelligent tracking control system for UAVs based on artificial intelligence image recognition. Background Art
[0002] With the rapid development of UAV technology, UAVs have been widely used in military reconnaissance, civilian surveillance, disaster relief and other fields. However, traditional UAV tracking control systems often rely on external devices such as radar and GPS, and have problems such as low tracking accuracy and susceptibility to interference. Therefore, it is of great significance to develop an intelligent tracking control system for UAVs based on image recognition, which can improve the accuracy and stability of tracking while reducing the dependence on external devices.
[0003] The purpose of the present invention is to provide an intelligent tracking control system and method for UAVs based on artificial intelligence image recognition. The system integrates advanced image recognition algorithms and UAV flight control technology to achieve precise recognition and tracking of target objects. Summary of the Invention
[0004] To overcome the defects in the background art, the present invention provides an intelligent tracking control system for UAVs based on artificial intelligence image recognition, including an image acquisition unit, an image recognition unit, a flight control unit and a communication unit. The characteristics are as follows: the image acquisition unit is used to obtain image data containing target objects in real time; the image recognition unit uses deep learning algorithms to extract features and recognize the image data, and outputs the position information of the target object; the flight control unit adjusts the flight attitude and trajectory of the UAV according to the position information output by the image recognition unit to achieve tracking of the target object; the communication unit is used to transmit data and control commands between the image acquisition unit, the image recognition unit, the flight control unit and external devices.
[0005] Preferably, a deep learning algorithm is provided in the image recognition unit, and the deep learning algorithm includes but is not limited to a convolutional neural network (CNN) or a recurrent neural network (RNN), which is used to improve the accuracy and robustness of image recognition.
[0006] Preferably, the flight control unit adopts an adaptive control algorithm to dynamically adjust the flight parameters of the UAV according to the real-time position information of the target object.
[0007] Preferably, the image acquisition unit obtains real-time image data containing target objects.
[0008] Preferably, the deep learning algorithm in the image recognition unit processes the image data, extracts the features of the target object and recognizes its position.
[0009] Preferably, the flight control unit adjusts the flight attitude and trajectory of the drone.
[0010] Preferably, the communication unit transmits the status information of the drone and the position information of the target object to an external device or a control system.
[0011] The present invention provides an intelligent tracking control system for a drone based on artificial intelligence image recognition. By integrating advanced image recognition algorithms and drone flight control technologies, this system realizes the precise recognition and tracking of target objects. The present invention has broad application prospects and important practical values. Specific embodiments
[0012] Specific Embodiment 1, an intelligent tracking control system for a drone based on artificial intelligence image recognition, includes an image acquisition unit, an image recognition unit, a flight control unit, and a communication unit. It is characterized in that: the image acquisition unit is used to obtain image data containing a target object in real time; the image recognition unit uses a deep learning algorithm to extract features and recognize the image data, and outputs the position information of the target object; the flight control unit adjusts the flight attitude and trajectory of the drone according to the position information output by the image recognition unit to achieve the tracking of the target object; the communication unit is used to transmit data and control instructions among the image acquisition unit, the image recognition unit, the flight control unit, and an external device. The image recognition unit is provided with a deep learning algorithm, and the deep learning algorithm includes, but is not limited to, a convolutional neural network (CNN) or a recurrent neural network (RNN), which is used to improve the accuracy and robustness of image recognition. The flight control unit adopts an adaptive control algorithm and dynamically adjusts the flight parameters of the drone according to the real-time position information of the target object. The image acquisition unit obtains real-time image data containing the target object, the deep learning algorithm in the image recognition unit processes the image data, extracts the features of the target object and recognizes its position, the flight control unit adjusts the flight attitude and trajectory of the drone, and the communication unit transmits the status information of the drone and the position information of the target object to an external device or a control system.
[0013] In a specific embodiment of the present invention, the image recognition unit uses a convolutional neural network (CNN) as the deep learning algorithm to extract features and recognize the image data. CNN has powerful image processing capabilities and can accurately extract the features of the target object and recognize its position. The flight control unit adopts an adaptive control algorithm and dynamically adjusts the flight parameters of the drone, such as speed, direction, etc., according to the real-time position information of the target object to achieve the precise tracking of the target object.
[0014] In the experimental verification, we used a drone equipped with a high-definition camera and installed the intelligent tracking control system described in the present invention. By setting the target object (such as pedestrians, vehicles, etc.), the system can obtain the image data of the target object in real time and perform recognition and processing through the CNN algorithm. The experimental results show that the system described in the present invention can accurately identify the position of the target object and control the drone for precise tracking, and both the tracking accuracy and stability are superior to the traditional methods.
[0015] In another specific embodiment, the image recognition unit uses a recurrent neural network (RNN) as the deep learning algorithm to process serialized image data. RNN can process image data with time series characteristics and is suitable for tracking dynamic targets. The flight control unit also uses an adaptive control algorithm to adjust the flight parameters of the drone according to the position information of the target object output by the RNN.
[0016] To verify the effectiveness of this embodiment, we conducted a series of experiments. The experimental results show that the RNN algorithm can accurately identify the position of the dynamic target and control the drone for continuous tracking. Even in a complex environment (such as occlusion, light change, etc.), the system described in the present invention can still maintain high tracking accuracy and stability.
[0017] In summary, the present invention provides an intelligent tracking control system for drones based on artificial intelligence image recognition. By integrating advanced image recognition algorithms and drone flight control technologies, the system realizes precise recognition and tracking of target objects. The present invention has broad application prospects and important practical values.
[0018] For those of ordinary skill in the art, other different forms of changes or modifications can be made based on the above description. It is not necessary and impossible to enumerate all the embodiments here. And the obvious changes or modifications derived therefrom are still within the protection scope of the present invention.
Claims
1. An intelligent tracking and control system for unmanned aerial vehicles based on artificial intelligence image recognition, comprising an image acquisition unit, an image recognition unit, a flight control unit and a communication unit, characterized in that: The image acquisition unit is used to acquire image data containing the target object in real time; the image recognition unit uses a deep learning algorithm to extract and recognize features of the image data and output the position information of the target object; the flight control unit adjusts the flight attitude and trajectory of the UAV according to the position information output by the image recognition unit to achieve tracking of the target object; the communication unit is used to transmit data and control instructions between the image acquisition unit, the image recognition unit, the flight control unit and the external device.
2. The system according to claim 1 is an intelligent tracking and control system for unmanned aerial vehicles based on artificial intelligence image recognition, characterized in that: The image recognition unit is provided with a deep learning algorithm, which includes but is not limited to a convolutional neural network (CNN) or a recurrent neural network (RNN) for improving the accuracy and robustness of image recognition.
3. The system according to claim 1 is an intelligent tracking and control system for unmanned aerial vehicles based on artificial intelligence image recognition, characterized in that: The flight control unit adopts an adaptive control algorithm to dynamically adjust the flight parameters of the UAV according to the real-time position information of the target object.
4. The system according to claim 1 is an intelligent tracking and control system for unmanned aerial vehicles based on artificial intelligence image recognition, characterized in that: The image acquisition unit acquires real-time image data containing the target object.
5. The system according to claim 1 is an intelligent tracking and control system for unmanned aerial vehicles based on artificial intelligence image recognition, characterized in that: The deep learning algorithm in the image recognition unit processes the image data, extracts the features of the target object and identifies its location.
6. The system according to claim 1 is a UAV intelligent tracking and control system based on artificial intelligence image recognition, characterized in that: The flight control unit adjusts the flight attitude and trajectory of the UAV.
7. The system according to claim 1 is an intelligent tracking and control system for unmanned aerial vehicles based on artificial intelligence image recognition, characterized in that: The communication unit transmits the status information of the drone and the location information of the target object to an external device or a control system.