Unmanned aerial vehicle detection method and equipment

Abstract

The invention relates to the technical field of unmanned aerial vehicle detection, and provides an unmanned aerial vehicle detection method and device, and the method comprises the steps: scanning a frequency band, scanning an image transmission signal of an unmanned aerial vehicle, and screening out a frequency point suspected to have an image signal according to the intensity of a received signal; video receiving: sequentially receiving the AV signals corresponding to the screened frequency points in a frequency matching manner, and switching one path of AV signal through a video signal switch every time and outputting the AV signal to a rear end; signal processing: converting the AV signal into a digital video signal through a video encoder, and outputting the digital video signal to an image processing module; image identification: after extracting a pixel matrix by an image processing module, judging whether the video signal belongs to real and effective image information; determining a target, and comprehensively judging whether a certain frequency point belongs to a signal transmitted by an image transmission of the unmanned aerial vehicle in combination with frequency point information identified by the main control unit; in combination with interference, the image processing module can also link the detection result with interference equipment. The detection efficiency of the unmanned aerial vehicle is improved, and the countering cost of the unmanned aerial vehicle is reduced.

Description

Technical Field

[0001] This invention relates to the technical field of drone detection, and provides a drone detection method and equipment. Background Technology

[0002] Drone detection in complex urban environments has always been a challenge for the industry. Currently, detecting drone flight control signals is the primary method for monitoring commercial drones. However, in complex urban terrain, these signals are easily blocked by tall buildings, and the flight control equipment is furthest from the detection system, making signal acquisition difficult. In contrast, image transmission signals are generated by the drone, are less prone to obstruction, and have higher signal power, making them easier to acquire. Therefore, detecting image transmission signals can effectively expand the working range of drone detection systems. Most mainstream commercial drones use specific radio frequency bands according to international standards or national regulations. However, if malicious actors with counter-surveillance capabilities modify the drone's image transmission signal to a less common frequency band, users will need to use equipment with wider coverage and more advanced technology for detection. Therefore, it is necessary to develop a wide-band image transmission signal detection system that covers most frequencies between 1GHz and 8GHz, effectively protecting the security of highly sensitive areas. Summary of the Invention

[0003] The technical problem solved by the present invention is to overcome the shortcomings of the prior art and provide a drone detection method and device that can quickly detect drone signals and effectively reduce the safety hazards that drones bring to society.

[0004] The technical solution of this invention is: a method for detecting unmanned aerial vehicles (UAVs), comprising: The frequency bands of the image transmission signals of drones currently flying within the detection range are scanned, and frequency points with image signals are selected based on the strength of the received drone image transmission signals. The selected frequency points are sequentially matched to receive the drone image transmission signals, and one frame of drone image transmission signal is switched and output to the back end each time through the video signal switch. A single frame of drone image transmission signal output to the backend is converted into a digital video signal; Image recognition is performed to extract the pixel matrix from the digital video signal and determine whether the current frame of the digital video signal is a real and valid image. If it is a real and valid image, subsequent processing is performed; otherwise, the current video signal is discarded. Determine whether the frequency point corresponding to the currently valid image information is within the frequency points that have image signals as filtered above. If it is, then the frequency point corresponding to the currently valid image information is the transmission frequency point used by the UAV image transmission signal.

[0005] Preferably, the currently valid UAV image transmission signal is displayed on the human-machine interface and simultaneously reported to the command and control center. As needed, the UAV is jammed at the corresponding frequency point in conjunction with jamming equipment.

[0006] Preferably, the frequency band scanning includes: Real-time scanning of frequency signals transmitted by multiple drones, including conventional and unconventional frequency bands within the 1-8 GHz range; Different analog voltage values ​​are output based on the strength of the image transmission signal, and then the frequency points with image signals are selected based on the magnitude of the voltage values.

[0007] Preferably, the video receiving includes: Align all frequency points containing image signals. The image signals with aligned frequency points are received sequentially; The received image signal is switched via a video signal switch to switch the drone image transmission signal output to the backend at different frequencies.

[0008] Preferably, the digital video signal is an RSTP data stream.

[0009] Preferably, the image recognition includes: When there is no video input at the back end, output a time-stamped, completely black reference video stream, and construct a real-time data stream by combining the completely black reference video stream and the input RSTP data stream; Pixel-level analysis is performed on each video stream frame in the real-time data stream. All pixel units within the frame are traversed, and the B, G, and R channel values ​​of each pixel unit are obtained. When B∈[0,120)∩G∈[0,120)∩R∈[0,120), it is marked as a black pixel. The proportion of black pixels P=(Σ number of black pixels) / resolution value is calculated. When P exceeds the set threshold θ, the current frame is determined to be an invalid image; otherwise, it is real and valid image information.

[0010] Preferably, the value of the threshold θ satisfies: θ = α × (1 + log2(w × h / 10) 6 )), where w and h are the video resolutions, and α is the compensation coefficient ∈ [0.85, 0.95].

[0011] Preferably, a video processing pipeline is built based on the OpenCV library, and the asynchronous capture of video stream frames is implemented through the VideoCapture class. The video stream frames are stored in a circular buffer in BGR color space. A drone detection device, comprising: The radio frequency signal receiving module scans the drone image transmission signal, filters out the frequency points with image signals based on the received signal strength, and sends them to the main control unit; it sequentially receives the drone image transmission signals corresponding to the filtered frequency points, and outputs one drone image transmission signal to the image processing unit each time by switching the video signal switch. The image processing unit processes each frame of the received UAV image transmission signal, converts the received UAV image transmission signal into a digital video signal, extracts the pixel matrix, and determines whether the video signal belongs to real and valid image information. If it belongs to real and valid image information, the frequency point of the image information is sent to the main control unit; otherwise, the current video signal is discarded. The main control unit identifies the received frequency point and determines whether it is within the frequency range of the image signal transmitted by the radio frequency signal receiving module. If it is, then the frequency point is the transmitting frequency point used by the UAV image transmission signal.

[0012] The device also includes a human-machine interaction unit. The main control unit sends the UAV image transmission signal corresponding to the identified frequency point to the human-machine interaction unit for display, and at the same time reports the identified frequency point to the command and control center. As needed, it works with jamming equipment to jam the UAV at the corresponding frequency point.

[0013] The advantages of this invention compared to the prior art are: This invention can quickly detect drone signals and analyze their location, model, communication frequency band, and other information through image transmission signal detection and identification methods. Combined with electromagnetic interference and other means, it enables efficient suppression and countermeasures against drones. This method is of great significance in the field of drone management to effectively reduce the security risks posed by drones to society. This invention improves drone detection efficiency and reduces the cost of drone countermeasures. Attached Figure Description

[0014] Figure 1 This is a flowchart of the UAV detection method of the present invention; Figure 2 This is a flowchart of the image recognition method of the present invention; Figure 3 This is a schematic diagram of the architecture of the UAV detection device of the present invention. Detailed Implementation

[0015] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments, such as... Figure 1 As shown, the specific steps include: 1.1 Frequency Band Scan The scan targets the frequency band of the image transmission signal of a drone currently flying within the detection range. The image transmission signal refers to the video image information transmitted between the drone and its remote controller.

[0016] Optionally, the specific preset frequency band covers the signal frequency band in the range of 1~8 GHz or scans the signal frequency band that can be covered by the drone's image transmission signal, thereby increasing the probability and quantity of receiving drone signals and reducing the missed detection rate of drone signals. The scanning frequency points include conventional frequency bands such as 1.2 GHz, 2.4 GHz, and 5.8 GHz in the range of 1~8 GHz, as well as unconventional frequency bands such as 1.3 GHz, 3.3 GHz, 4.9 GHz, and 8 GHz.

[0017] Optionally, the radio frequency signal receiving unit determines the signal strength of a frequency band when it scans the band. Specifically, it can convert the image transmission signal strength of the drone into an analog voltage value, and then determine the frequency location of the received signal based on the reading of the analog voltage value, thereby filtering out the frequency points containing image signals.

[0018] 1.2 Video Reception The received information is video information at the frequency point of the UAV image transmission information scanned during the frequency band scanning step.

[0019] Optionally, the video reception step via the radio frequency receiving unit is independent of the frequency band scanning step, and the two functional groups are laid out separately. This enables rapid scanning and rapid reception of the transmitted video signal, thereby further accelerating the resolution process of the transmitted video signal.

[0020] 1.3 Signal Processing The process involves processing the drone image transmission video information received during the video receiving step.

[0021] Optionally, the received analog video information is converted into an RSTP data stream that can be recognized and processed by the image processing unit through a video encoder.

[0022] 1.4 Image Recognition The identified data is the RSTP data stream of drone image transmission video information pushed in during the signal processing step.

[0023] In another embodiment of the present invention, a specific implementation of step 1.4 is as follows: Figure 2 As shown, it includes the following steps: 1.4.1 Establishing a real-time data stream Optionally, the video encoder is specifically configured to output a completely black reference video stream when there is no video input. The reference video stream includes an ISO 8601 format timestamp superimposed on the upper right corner of the screen via an OSD module.

[0024] Optionally, a video processing pipeline can be built based on the OpenCV library, and the asynchronous capture of video stream frames can be achieved through the VideoCapture class. The video stream frames are stored in a circular buffer in BGR color space.

[0025] Here, B, G, and R represent the proportions of the three basic color components—blue, green, and red—in the color space. By comparing and analyzing these values, the color patterns and characteristics present in the image can be inferred.

[0026] 1.4.2 Determination of Black Pixels Optionally, pixel-level analysis is performed on each video stream frame, traversing all pixel units within the frame and obtaining the B, G, and R channel values ​​of each pixel unit; when B∈[0,120)∩G∈[0,120)∩R∈[0,120), it is marked as a black pixel.

[0027] 1.4.3 Valid Image Determination Optionally, the proportion of black pixels P is specifically calculated as P = (Σ number of black pixels) / resolution value. When P does not exceed a set threshold θ, the current frame is determined to be a valid image.

[0028] Wherein, the value of the threshold θ satisfies: θ = α × (1 + log2(w × h / 10) 6 )), where w and h are the video resolutions, and α is the compensation coefficient ∈ [0.85, 0.95].

[0029] When the P value exceeds the set threshold θ, it means that the proportion of black pixels in an image is too high, thus the image is judged to be black, and the frame is determined to be invalid with no image input.

[0030] 1.5 Define the objectives Here, the determined components are the video frames that are identified as valid images in the image recognition step.

[0031] Optionally, the frequency information corresponding to the video frame is read and compared with the frequency information identified by the main control unit. If the frequency information is the same, it is determined that the frequency is the video signal transmitted by the UAV image transmission.

[0032] The frequency information identified by the main control unit is the frequency information scanned in the frequency band scanning in step 1.1.

[0033] 1.6 Joint Interference The target of the interference is to determine the frequency point of the UAV image transmission signal determined in the target step.

[0034] Optionally, the video signal transmitted by the UAV can be transmitted to a human-machine interface for observation by front-line users, or it can be transmitted to the command and control center to assist in overall scheduling, determine whether to conduct joint interference, and strike and control the UAV.

[0035] Another embodiment of the present invention provides a drone detection device, such as... Figure 3 As shown, it includes: The radio frequency receiving unit is mainly responsible for tasks such as scanning the frequency band of image transmission signals and receiving, switching and outputting video signals. The main control unit is primarily responsible for tasks such as test process control, function control, detection data processing, and communication. The image processing unit is mainly responsible for tasks such as video signal conversion, image processing and recognition. The human-computer interaction unit includes buttons and a display screen. Function selection is achieved through touch buttons, and the final results of image transmission detection and battery level are displayed on the screen. The power system is responsible for power supply and power management for the entire system. The main body is used to install and fix components such as the control board, display screen, buttons, external interface connectors, batteries, and antennas.

[0036] Specifically, the radio frequency signal receiving module of this invention scans the drone image transmission signal, filters out frequency points with image signals based on the received signal strength, and sends them to the main control unit; it sequentially receives the drone image transmission signals corresponding to the filtered frequency points, and switches one drone image transmission signal at a time through a video signal switch to output to the image processing unit; the image processing unit processes each frame of the received drone image transmission signal, converts the received drone image transmission signal into a digital video signal, extracts the pixel matrix, and determines whether the video signal belongs to real and valid image information; if it belongs to real and valid image information, it sends the frequency point of the image information to the main control unit; otherwise, it discards the current video signal; the main control unit identifies the received frequency point and determines whether it is within the frequency point range of image signals sent by the radio frequency signal receiving module. If it is, then the frequency point is the transmission frequency point used by the drone image transmission signal.

[0037] Using the architecture of this invention, each functional unit can be placed in a structure with dimensions within 375mm×200mm×60mm to form a handheld detection device; alternatively, each functional unit can be placed in a larger structure to form a pole-mounted detection device.

[0038] The parts of this invention not described in detail are common knowledge to those skilled in the art.

Claims

1. A method for detecting unmanned aerial vehicles (UAVs), characterized in that, include: The frequency bands of the image transmission signals of drones currently flying within the detection range are scanned, and frequency points with image signals are selected based on the strength of the received drone image transmission signals. The selected frequency points are sequentially received by the drone image transmission signals, and one frame of drone image transmission signal is switched and output to the back end each time through the video signal switch. A single frame of drone image transmission signal output to the backend is converted into a digital video signal; Image recognition is performed to extract the pixel matrix from the digital video signal and determine whether the digital video signal of the frame belongs to real and valid image information. If it belongs to real and valid image information, subsequent processing is performed. Otherwise, abandon the current video signal; Determine whether the frequency point corresponding to the currently valid image information is within the frequency points that have image signals as filtered above. If it is, then the frequency point corresponding to the currently valid image information is the transmission frequency point used by the UAV image transmission signal.

2. The method according to claim 1, characterized in that: The currently valid drone image transmission signal is displayed on the human-machine interface and reported to the command and control center. As needed, the center will coordinate with jamming equipment to jam the drone at the corresponding frequency.

3. The method according to claim 1, characterized in that, The frequency band scanning includes: Real-time scanning of frequency signals transmitted by multiple drones, including conventional and unconventional frequency bands within the 1-8 GHz range; Different analog voltage values ​​are output based on the strength of the image transmission signal, and then the frequency points with image signals are selected based on the magnitude of the voltage values.

4. The method according to claim 1, characterized in that, The video reception includes: Align all frequency points containing image signals. The image signals with aligned frequency points are received sequentially; The received image signal is switched via a video signal switch to switch the drone image transmission signal output to the backend at different frequencies.

5. The method according to claim 1, characterized in that, The digital video signal is an RSTP data stream.

6. The method according to claim 5, characterized in that, The image recognition includes: When there is no video input at the back end, output a time-stamped, completely black reference video stream, and construct a real-time data stream by combining the completely black reference video stream and the input RSTP data stream; Pixel-level analysis is performed on each video stream frame in the real-time data stream. All pixel units within the frame are traversed, and the B, G, and R channel values ​​of each pixel unit are obtained. When B∈[0,120)∩G∈[0,120)∩R∈[0,120), it is marked as a black pixel. The proportion of black pixels P=(Σ number of black pixels) / resolution value is calculated. When P exceeds the set threshold θ, the current frame is determined to be an invalid image; otherwise, it is real and valid image information.

7. The method according to claim 6, characterized in that, The value of the threshold θ satisfies: θ = α × (1 + log2(w × h / 10) 6 )), where w and h are the video resolutions, and α is the compensation coefficient ∈ [0.85, 0.95].

8. The method according to claim 6, characterized in that, A video processing pipeline is built based on the OpenCV library, and the asynchronous capture of video stream frames is achieved through the VideoCapture class. The video stream frames are stored in a circular buffer in BGR color space.

9. A drone detection device, characterized in that, include: The radio frequency signal receiving module scans the image transmission signal of the UAV, filters out the frequency points with image signals based on the received signal strength, and sends them to the main control unit. The selected frequency points are sequentially received by the drone image transmission signals, and one drone image transmission signal is switched and output to the image processing unit each time through the video signal switch. The image processing unit processes each frame of the received UAV image transmission signal, converts the received UAV image transmission signal into a digital video signal, extracts the pixel matrix, and determines whether the video signal belongs to real and valid image information. If the image information is genuine and valid, then the frequency point of the image information is sent to the main control unit; Otherwise, abandon the current video signal; The main control unit identifies the received frequency point and determines whether it is within the frequency range of the image signal transmitted by the radio frequency signal receiving module. If it is, then the frequency point is the transmitting frequency point used by the UAV image transmission signal.

10. The device according to claim 9, characterized in that: It also includes a human-machine interaction unit. The main control unit sends the drone image transmission signal corresponding to the identified frequency point to the human-machine interaction unit for display. At the same time, it reports the identified frequency point to the command and control center. As needed, it works with jamming equipment to jam the drone at the corresponding frequency point.

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