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Image fast Fourier transformation (FFT) symbol information based unmanned aerial vehicle autonomous landing target detection method

A technology of autonomous landing and target detection, applied in the field of drones, can solve problems such as loss and limited range of DCT, and achieve high accuracy, easy implementation, and simple principles

Active Publication Date: 2015-03-04
NAT UNIV OF DEFENSE TECH
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Problems solved by technology

There will be losses when using DCT processing for data that is not strictly real and even. Since the image data is not necessarily real and even, the scope of use of DCT is limited.

Method used

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  • Image fast Fourier transformation (FFT) symbol information based unmanned aerial vehicle autonomous landing target detection method
  • Image fast Fourier transformation (FFT) symbol information based unmanned aerial vehicle autonomous landing target detection method
  • Image fast Fourier transformation (FFT) symbol information based unmanned aerial vehicle autonomous landing target detection method

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Embodiment Construction

[0026] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0027] like figure 1 Shown, the UAV autonomous landing target detection method based on image FFT symbol information of the present invention, its steps are:

[0028] (1), image I will be obtained num Convert from RGB color space to LAB color space;

[0029] Since the LAB color space is more consistent with the selection mechanism of human visual attention, the image is generally converted from the RGB of the computer to LAB first.

[0030] (2), carry out FFT transformation to each channel of LAB respectively, obtain corresponding amplitude spectrum and phase spectrum; That is: to obtain image I num The three channels of the LAB are respectively Fourier transformed to obtain the FFT processing result I of each channel num_i ,i=1,2,3.

[0031] The definition of FFT transform is:

[0032] F ( ...

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Abstract

The invention discloses an image FFT symbol information based unmanned aerial vehicle autonomous landing target detection method. The method comprises the steps of (1) converting RGB color space of an obtained image Inum into LAB color space; (2) performing FFT to obtain an image Inum FFT processing result Inum_FFT; (3) performing symbol information extraction on the transformed image, that is, performing symbolic operation on the image Inum_FFT to obtain a symbolic operation result Inum_SIGN; (4) performing Fourier inversion on the image processed through a symbolic operator to obtain an IFFT processing result Inum_IFFT; (5) performing Gaussian convolution smoothing, and performing smoothing filtering on the inversion image to obtain a significant image Inum_SIG; (6) performing area selection on the significant image through a threshold to obtain a selected image Inum1; (7) averaging coordinates of the unmanned aerial vehicle area in the Inum1 to serve as the location coordinate (xnum_out, ynum_out) of the unmanned aerial vehicle. The method has the advantages of being simple in principle, easy to implement, high in accuracy and the like.

Description

technical field [0001] The invention mainly relates to the field of unmanned aerial vehicles, in particular to a method for detecting an autonomous landing target of an unmanned aerial vehicle based on image FFT symbol information. Background technique [0002] UAV autonomous landing technology using vision guidance is an important means of autonomous UAV recovery. Drone detection technology plays an important role in vision guidance technology. In order to realize the autonomous landing of the UAV, it is necessary to detect and locate the UAV from the images obtained by the camera on the ground turntable. How to design an algorithm with good robustness, high detection accuracy, and strong real-time Key issues that need to be addressed for landing. [0003] Due to the visual attention mechanism of the human eye, humans can easily detect salient areas in the scene and quickly obtain valuable information. Visual attention algorithms can automatically predict the location of...

Claims

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Application Information

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IPC IPC(8): G06K9/46G06K9/54
CPCG06T7/11G06T7/262G06T2207/10016G06T2207/20056G06T2207/20182
Inventor 牛轶峰沈镒峰沈林成
Owner NAT UNIV OF DEFENSE TECH
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