CN114170260B - Real-time image blade segmentation system and method based on vegetation index - Google Patents
Real-time image blade segmentation system and method based on vegetation index Download PDFInfo
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Abstract
The invention discloses a real-time image blade segmentation system and a method based on vegetation indexes, wherein the system comprises the following steps: the vegetation index calculation module is used for calculating a vegetation index according to the vegetation index of the vegetation index; the method comprises the following steps: receiving a control instruction, a video stream and scaling factor parameters, calculating each vegetation index according to the control instruction and the scaling factor parameters, dividing each vegetation index into a group of three, respectively writing each vegetation index into three channels of red R, green G and blue B, and generating an RGB color video stream as a vegetation index image to output; receiving a vegetation index image and realizing caching of the vegetation index image according to a control instruction; and carrying out leaf segmentation on the vegetation index image according to a preset judgment threshold value. The blade segmentation system and the blade segmentation method provided by the invention have the advantages of low cost, high efficiency, no need of training a model in advance and strong practicability.
Description
Technical Field
The invention relates to the technical field of image segmentation, in particular to a vegetation index-based real-time image blade segmentation system and a vegetation index-based real-time image blade segmentation method.
Background
Image segmentation is a technique and process of dividing an image into a number of specific regions with unique properties and presenting objects of interest, which is a key step from image processing to image analysis. The image segmentation methods commonly used at present comprise a threshold image segmentation method, an edge segmentation method, a clustering segmentation method and other non-neural network methods, and a coding and decoding method, a multi-scale information integration method and other neural network methods. However, the conventional decoding method and the multi-scale information integration method all require huge neural networks, require a large amount of operation resources, require pre-training, and have high cost for preparing training sets and manually labeling data.
According to the spectral characteristics of vegetation, the spectral bands are combined to form a vegetation index, which is a simple and effective measure of the surface vegetation condition and is widely used in global and regional land coverage, vegetation classification and environmental changes. The vegetation index commonly used at present needs to be synthesized by a visible light wave band and a near infrared wave band, and an expensive near infrared camera needs to be additionally added to obtain near infrared, so that the cost is increased. At present, a plant leaf segmentation method utilizing a visible light vegetation index is not available.
Therefore, how to provide a low-cost and efficient real-time image vane segmentation system and method based on vegetation indexes is a problem that needs to be solved by those skilled in the art.
Disclosure of Invention
In view of the above, the present invention provides a real-time image vane segmentation system and method based on vegetation index
In order to achieve the above purpose, the present invention adopts the following technical scheme:
A real-time image leaf segmentation system based on a vegetation index, comprising: the vegetation index calculation module is used for calculating a vegetation index according to the vegetation index of the vegetation index;
The kernel control module is used for receiving the control instruction and controlling the kernel to control other modules according to the control instruction;
The vegetation index calculation module is connected with the kernel and the VDMA module and is used for receiving video stream and scaling factor parameters, calculating each vegetation index according to the scaling factor parameters, dividing each vegetation index into a group, respectively writing each vegetation index into three channels of red R, green G and blue B, and generating RGB color video stream as vegetation index image output;
The VDMA module is connected with the kernel and is used for receiving the vegetation index image and realizing caching of the vegetation index image according to a control instruction received by the kernel;
The threshold segmentation module is connected with the kernel and the vegetation index calculation module, and performs blade segmentation on the vegetation index image according to a preset judgment threshold.
Preferably, the kernel control module comprises a DDR3 controller, a memory card management unit and a serial port unit;
the DDR3 controller is respectively connected with the DDR3 running memory, is connected with the vegetation index calculation module through the VDMA module, and is used for receiving the vegetation index image, caching the vegetation index image on the DDR3 running memory in real time in a direct memory access mode through the VDMA module, and notifying the kernel that caching is finished after caching is finished;
the memory card management unit is respectively connected with the VDMA module and the kernel and is connected with an external memory card, and is used for storing the acquired vegetation index image into the external memory card through the VDMA module and notifying the kernel of finishing the preservation after the preservation is finished;
the serial port unit is used for realizing communication between the kernel and the external equipment in a serial port mode and receiving control instructions and scaling factor parameters.
Preferably, the vegetation index calculating module comprises a fan-out processing unit, a vegetation index calculator, a fan-in processing unit and a multiplexer;
the fan-out processing unit is used for receiving the video stream, and carrying out fan-out processing on the video stream to obtain three independent RGB color channels;
The vegetation index calculator is used for receiving the scaling factor parameters and calculating the values of vegetation indexes by combining the scaling factor parameters;
The fan-in processing unit is used for dividing each vegetation index into a group of every three, and writing the vegetation indexes into three channels of red R, green G and blue B respectively to obtain N paths of RGB color video streams;
the multiplexer is used for selecting one path from the N paths of RGB color video streams to output as the vegetation index image.
Preferably, the VDMA module includes an S2MM channel and an MM2S channel therein;
The S2MM channel is used for providing a channel for directly accessing the memory to cache the real-time processing result;
and the MM2S channel is used for providing a path for storing the cached processing result to an external memory card.
Preferably, the device further comprises an HDMI decoder and an HDMI encoder;
The HDMI decoder is respectively connected with an external image acquisition device and the vegetation index calculation module, and is used for receiving the real-time video stream acquired by the external image acquisition device through an HDMI interface, converting the real-time video stream into a VESA standard video stream, and sending the converted video stream to the vegetation index calculation module according to a control instruction of the kernel control module;
the HDMI encoder is used for outputting the processed vegetation index image to the downlink equipment through an HDMI interface.
Preferably, the threshold segmentation module sets decision thresholds for three color channels of RGB respectively, performs logical AND operation on areas in the three color channel thresholds, obtains a mask of the position of the blade in the input image, and realizes segmentation of the blade image.
Preferably, the vegetation index image processing device further comprises a filtering module, wherein the filtering module is used for carrying out histogram equalization and median filtering on the vegetation index image;
the specific processing procedure of the histogram equalization is as follows:
Wherein, H in represents the frequency histogram of one color channel in an input frame of image, and is obtained by counting the gray frequency of the corresponding color channel in the frame of image; l max represents the maximum luminance value; size indicates how many pixels there are in a frame of image; l in is the gray value of a pixel point in the input image; l out represents the processing result pixel value of the corresponding pixel after the histogram equalization processing;
Respectively carrying out histogram equalization on three RGB channels of each frame of image, and combining processing results of the three channels at an output end to form histogram equalization of a vegetation index group;
the specific processing procedure of the median filtering processing is as follows:
Wherein, P1, P2, …, P9 are adjacent 3*3 pixel matrixes; max1, max2, max3 respectively refer to the maximum values of the first, second, and third rows in 3*3 pixel matrices; mid1, mid2, mid3 refer to the intermediate values of the first, second, and third rows in the 3*3 pixel matrix, respectively; min1, min2 and Min3 refer to the minimum values of the first, second and third rows in the 3*3 pixel matrix respectively; max_min is the minimum value of Max1, max2 and Max 3; max_mid is the intermediate value among Mid1, mid2, mid3 described above; min_max is the minimum value of Max1, max2 and Max 3; the median filtering results are the median of max_min, mid_mid, and min_max.
A real-time image vane segmentation method based on vegetation indexes comprises the following steps:
Receiving a control instruction, a video stream and a scaling factor parameter, calculating each vegetation index according to the control instruction and combining the scaling factor parameter, dividing each vegetation index into a group, respectively writing each vegetation index into three channels of red R, green G and blue B, and generating an RGB color video stream as a vegetation index image to output;
Receiving the vegetation index image and caching the vegetation index image according to the control instruction;
and carrying out leaf segmentation on the vegetation index image according to a preset judgment threshold value.
Compared with the prior art, the invention discloses a real-time image blade segmentation system and a method based on the vegetation index, wherein the vegetation index is used as the basis of image segmentation, the real-time performance is high, the occupied resources are low, the system can be used without pre-training, and the output image can be stored in a memory card, so that the control and the processing result of equipment can be conveniently exported.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a schematic diagram of the overall structure of a real-time image vane segmentation system based on vegetation indexes according to the present invention;
FIG. 2 is a schematic diagram of a vegetation index calculation module in a real-time image vane segmentation system based on a vegetation index according to the present invention;
FIG. 3 is a schematic diagram of a filter in a real-time image vane segmentation system based on vegetation indexes according to the present invention;
FIG. 4 is a schematic diagram of a kernel module in the vegetation index-based real-time image vane segmentation system according to the present invention;
FIG. 5 is a flowchart showing the operation of buffering a single image in a real-time image vane segmentation system based on vegetation index according to the present invention;
fig. 6 is a schematic flow chart of a histogram equalization process in a real-time image vane segmentation system based on vegetation indexes.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The embodiment of the invention discloses a real-time image blade segmentation system based on vegetation indexes, which is shown in fig. 1 and comprises the following steps: the vegetation index calculation module is used for calculating a vegetation index according to the vegetation index of the vegetation index;
the kernel control module is used for receiving the control instruction and controlling the kernel to control other modules according to the control instruction;
The vegetation index calculating module is connected with the kernel and the VDMA module and is used for receiving the video stream and the scaling factor parameters, calculating each vegetation index according to the scaling factor parameters, dividing each vegetation index into a group, respectively writing each vegetation index into three channels of red R, green G and blue B, and generating RGB color video stream as vegetation index image output;
The VDMA module is connected with the kernel and is used for receiving the vegetation index image and realizing caching of the vegetation index image according to a control instruction received by the kernel;
The threshold segmentation module is connected with the kernel and the vegetation index calculation module, and performs leaf segmentation on the vegetation index image according to a preset judgment threshold.
In order to further implement the technical scheme, the kernel control module comprises a DDR3 controller, a memory card management unit and a serial port unit;
The DDR3 controller is respectively connected with the DDR3 operation memory, is connected with the vegetation index calculation module through the VDMA module, and is used for receiving the vegetation index image, caching the vegetation index image on the DDR3 operation memory in real time in a direct memory access mode through the VDMA module, and notifying the kernel of finishing caching after finishing caching;
The memory card management unit is respectively connected with the VDMA module and the kernel and is connected with an external memory card, and is used for storing the acquired vegetation index image into the external memory card through the VDMA module and notifying the kernel of finishing the preservation after the preservation is finished;
And the serial port unit is used for realizing communication between the kernel and the external equipment in a serial port mode and receiving control instructions and scaling factor parameters.
In order to further implement the technical scheme, the vegetation index calculating module comprises a fan-out processing unit, a vegetation index calculator, a fan-in processing unit and a multiplexer;
the fan-out processing unit is used for receiving the video stream, and carrying out fan-out processing on the video stream to obtain three independent RGB color channels;
A vegetation index calculator for receiving the scaling factor parameter and calculating a vegetation index value in combination with the scaling factor parameter;
the fan-in processing unit is used for dividing each vegetation index into a group, and writing the vegetation indexes into three channels of red R, green G and blue B respectively to obtain N paths of RGB color video streams;
and the multiplexer is used for selecting one path from the N paths of RGB color video streams to be output as a vegetation index image.
In this embodiment, as shown in fig. 2, the principle of the vegetation index calculating module is specifically described, and the vegetation index calculating module first performs fan-out processing on the RGB video stream of the incoming VESA video stream to obtain three separate color channels of RGB. The vegetation index calculator obtains the scaling factor parameters required by calculation through an AXI4-Lite control bus, calculates NPCI, NGBDI, NGRDI, RGBVI, MGRVI, VDVI six vegetation indexes by combining the parameters, takes NPCI, NGBDI, NGRDI as a group, RGBVI, MGRVI, VDVI as a group, and obtains two paths of RGB color images by utilizing an RGB fan-in module. And selecting one path of the multiplexer to output in a VESA video stream mode under the control of the AXI4-Lite bus.
The working principles of each part of the vegetation index calculating module in this embodiment are as follows:
The improved vegetation index is used as a core basis for image segmentation, and the calculation formula is as follows:
Vegetation index set 1:
wherein R, G, B are the red, green and blue channels of the input video stream, respectively; alpha is the scaling factor of the vegetation index group 1; out_ R, out _ G, out _b is the red, green, and blue channels of the processing result video output, respectively.
For the vegetation index group 1, in order to achieve the above calculation, the following two steps are needed:
1)C1＝α*R C2＝α*G C3＝R+B C4＝G+B C5＝G+R
2)E1＝C1/C3 E2＝C2/C4 E3＝C2/C5
wherein, C and D are both intermediate results, E is the calculation result of the final vegetation index group 1.
Vegetation index group 2:
wherein R, G, B are the red, green and blue channels of the input video stream, respectively; beta is the scaling factor of the vegetation index group 2; out_ R, out _ G, out _b is the red, green, and blue channels of the processing result video output, respectively.
For the vegetation index group 2, the above calculation is performed in three steps:
1)C1＝G2 C2＝R2 C3＝R*B C4＝2*β*G C5＝R+B C6＝2*G
2)D1＝β*C1 D2＝C1+C3 D3＝C1+C2 D4＝C5+C6 D5＝C4
3)E1＝D1/D2 E2＝D1/D3 E3＝D5/D4
Wherein, C and D are both intermediate calculation results, E is the calculation result of the final vegetation index group 2.
In order to further implement the technical scheme, the VDMA module comprises an S2MM channel and an MM2S channel;
the S2MM channel is used for providing a channel for directly accessing the memory to cache the real-time processing result;
And the MM2S channel is used for providing a path for storing the cached processing result to an external memory card.
In order to further implement the technical scheme, the device also comprises an HDMI decoder and an HDMI encoder;
The HDMI decoder is respectively connected with the external image acquisition equipment and the vegetation index calculation module, and is used for receiving the real-time video stream acquired by the external image acquisition equipment through the HDMI interface, converting the real-time video stream into a VESA standard video stream, and sending the converted video stream to the vegetation index calculation module according to the control instruction of the kernel control module;
the HDMI encoder is used for outputting the processed vegetation index image to the downlink equipment through the HDMI interface.
In order to further implement the technical scheme, the threshold segmentation module sets decision thresholds for three RGB color channels respectively, performs logical AND operation on areas in the three color channel thresholds to obtain a mask of the position of the blade in the input image, and achieves segmentation of the blade image.
In order to further implement the technical scheme, the vegetation index image processing system further comprises a filtering module, a processing module and a processing module, wherein the filtering module is used for carrying out histogram equalization and median filtering processing on the vegetation index image;
the specific processing procedure of the histogram equalization is as follows:
Wherein, H in represents the frequency histogram of one color channel in an input frame of image, and is obtained by counting the gray frequency of the corresponding color channel in the frame of image; l max represents the maximum luminance value; size indicates how many pixels there are in a frame of image; l in is the gray value of a pixel point in the input image; l out represents the processing result pixel value of the corresponding pixel after the histogram equalization processing;
as shown in fig. 3, the specific processing procedure of the median filtering process is:
Wherein, P1, P2, …, P9 are adjacent 3*3 pixel matrixes; max1, max2, max3 respectively refer to the maximum values of the first, second, and third rows in 3*3 pixel matrices; mid1, mid2, mid3 refer to the intermediate values of the first, second, and third rows in the 3*3 pixel matrix, respectively; min1, min2 and Min3 refer to the minimum values of the first, second and third rows in the 3*3 pixel matrix respectively; max_min is the minimum value of Max1, max2 and Max 3; max_mid is the intermediate value among Mid1, mid2, mid3 described above; min_max is the minimum value of Max1, max2 and Max 3; the median filtering results are the median of max_min, mid_mid, and min_max.
In this embodiment, as shown in FIG. 4, contex-A9 is selected as the kernel; wherein:
The Contex-A9 kernel communicates with the external control equipment (such as the upper computer of the computer end) through the serial port unit, the user can send the control command through the serial port, the control command includes setting vegetation index and calculating the scaling factor, choosing vegetation and calculating and outputting group, filter and outputting group and shooting command; in addition, the current working state of the system can be read through the serial port.
And the Contex-A9 kernel is in bidirectional communication with the serial port unit, the VDMA module, the memory card management unit, the filter module and the vegetation index calculation module through an AXI4-Lite bus, and when the device is powered on, the Contex-A9 kernel initializes each module through the bus.
Referring to fig. 5, when the system is powered on, the VDMA module and the memory card management module initialize, and after initialization is completed, an initialization completion notification is sent to the Cortex-A9 kernel, and the kernel enters a photographing instruction waiting state. After receiving an external photographing instruction, the kernel informs the VDMA module of image caching, and the VDMA module starts waiting for a field synchronizing signal. After the field synchronizing signal is finished, a new frame of image is started, the VDMA module caches the frame of image into the DDR3 memory through the DDR3 controller, and after the caching is finished, the kernel image is informed of finishing caching. After the kernel receives the image caching completion notification, the kernel commands the memory card control module to write the image into the memory card, and meanwhile, the VDMA module sends an image data stream to the memory card control module. After the memory card finishes storing the image, the kernel image is informed of finishing storing the image, and the photographing state is finished. And after ending, the VDMA module and the memory card control module inform the kernel of ending the photographing process once.
In this embodiment, histogram equalization is a method for adjusting contrast by using image histograms in the field of image processing, so that brightness can be better distributed on the histograms, thereby enhancing local contrast without affecting overall contrast. In the hardware implementation, for each channel of the three RGB channels, firstly, counting 256-level gray frequency of a frame of picture of the corresponding channel to obtain a frequency histogram, and summing the variable upper limit of the frequency histogram to obtain a cumulative distribution function in the field synchronization period after the end of the frame of picture, wherein the formula of the process is as follows:
wherein hk represents the frequency of occurrence of the kth gray in the gray frequency histogram; h t represents the accumulated value of the t-th gray scale of the accumulated pattern.
After field synchronization, a new frame of image arrives, and each pixel point of the image is processed as follows:
Wherein H [ L ] represents an accumulated value of the L-th gradation of the accumulated pattern; l max is the maximum gray value; size is the number of pixels of one frame image; f (L) is the output of the algorithm when the gray value of the corresponding pixel point is L.
After each pixel point is processed, the output image is the histogram equalization result. And when the video stream is output, counting the frequency histogram of the current frame image for the histogram equalization of the next frame image, and thus, performing reciprocating so as to realize the histogram equalization filtering of the video stream.
Before processing, counting the gray frequency of an image to obtain a frequency histogram before processing, accumulating the frequency histogram to obtain a cumulative distribution function H, obtaining a conversion function f for the image through a formula, and performing histogram equalization filtering on each pixel point of the image by using the conversion function to obtain a processed image. The processed image is more obvious in light-dark distribution compared with the processed image, the histogram equalization filtering algorithm plays a role in diverging the frequency histogram, and the processed frequency histogram is full of the whole color gradation. As shown in fig. 6.
And respectively carrying out histogram equalization on the three RGB channels of each frame of image, and combining the processing results of the three channels at an output end to form the histogram equalization of the vegetation index group.
A real-time image vane segmentation method based on vegetation indexes comprises the following steps:
Receiving a control instruction, a video stream and scaling factor parameters, calculating each vegetation index according to the control instruction and the scaling factor parameters, dividing each vegetation index into a group of three, respectively writing each vegetation index into three channels of red R, green G and blue B, and generating an RGB color video stream as a vegetation index image to output;
receiving a vegetation index image and realizing caching of the vegetation index image according to a control instruction;
And carrying out leaf segmentation on the vegetation index image according to a preset judgment threshold value.
The invention uses the vegetation index as the basis of image segmentation, has high instantaneity and low occupied resources, and can be used without pre-training. And secondly, an image filtering processing method of histogram equalization filtering and median filtering is designed, so that the image can be further processed according to requirements before being output. And the output image can be stored in the memory card by serial communication with external control equipment, so that the control and the processing result of the equipment are conveniently exported.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. A real-time image leaf segmentation system based on a vegetation index, comprising: the vegetation index calculation module is used for calculating a vegetation index according to the vegetation index of the vegetation index;
The kernel control module is used for receiving the control instruction and controlling the kernel to control other modules according to the control instruction;
The vegetation index calculation module is connected with the kernel and the VDMA module and is used for receiving video stream and scaling factor parameters, calculating each vegetation index according to the scaling factor parameters, dividing each vegetation index into a group, respectively writing each vegetation index into three channels of red R, green G and blue B, and generating RGB color video stream as vegetation index image output;
The vegetation index calculating module comprises a fan-out processing unit, a vegetation index calculator, a fan-in processing unit and a multiplexer;
the fan-out processing unit is used for receiving the video stream, and carrying out fan-out processing on the video stream to obtain three independent RGB color channels;
the vegetation index calculator is used for receiving the scaling factor parameters and calculating the values of vegetation indexes by combining the scaling factor parameters; the vegetation index includes NPCI, NGBDI, NGRDI, RGBVI, MGRVI and VDVI;
The fan-in processing unit is used for dividing each vegetation index into a group of every three, and writing the vegetation indexes into three channels of red R, green G and blue B respectively to obtain N paths of RGB color video streams; wherein NPCI, NGBDI, NGRDI is vegetation index set 1, RGBVI, MGRVI, VDVI is vegetation index set 2;
The multipath selector is used for selecting one path from N paths of RGB color video streams to output as the vegetation index image;
the improved vegetation index specifically includes:
Vegetation index set 1:
wherein R, G, B are the red, green and blue channels of the input video stream, respectively; alpha is the scaling factor of the vegetation index group 1; out_ R, out _ G, out _B is respectively the three red, green and blue channels of the video output of the processing result;
the method comprises the following two steps:
C1＝α*R,C2＝α*G,C3＝R+B,C4＝G+B,C5＝G+R
E1＝C1/C3,E2＝C2/C4,E3＝C2/C5
Wherein, C and D are both intermediate results, E is the calculation result of the final vegetation index group 1;
vegetation index group 2:
Wherein R, G, B are the red, green and blue channels of the input video stream, respectively; beta is the scaling factor of the vegetation index group 2; out_ R, out _ G, out _B is respectively the three red, green and blue channels of the video output of the processing result;
The method comprises the following three steps:
C1＝G2,C2＝R2,C3＝R*B,C4＝2*β*G,C5＝R+B,C6＝2*G
D1＝β*C1,D2＝C1+C3,D3＝C1+C2,D4＝C5+C6,D5＝C4
E1＝D1/D2,E2＝D1/D3,E3＝D5/D4
Wherein, C and D are both intermediate calculation results, E is the calculation result of the final vegetation index group 2;
The VDMA module is connected with the kernel and is used for receiving the vegetation index image and realizing caching of the vegetation index image according to a control instruction received by the kernel;
The threshold segmentation module is connected with the kernel and the vegetation index calculation module, and performs blade segmentation on the vegetation index image according to a preset judgment threshold.
2. The vegetation index based real-time image vane segmentation system of claim 1 wherein the kernel control module comprises a DDR3 controller, a memory card management unit, and a serial port unit;
the DDR3 controller is respectively connected with the DDR3 running memory, is connected with the vegetation index calculation module through the VDMA module, and is used for receiving the vegetation index image, caching the vegetation index image on the DDR3 running memory in real time in a direct memory access mode through the VDMA module, and notifying the kernel that caching is finished after caching is finished;
the memory card management unit is respectively connected with the VDMA module and the kernel and is connected with an external memory card, and is used for storing the acquired vegetation index image into the external memory card through the VDMA module and notifying the kernel of finishing the preservation after the preservation is finished;
the serial port unit is used for realizing communication between the kernel and the external equipment in a serial port mode and receiving control instructions and scaling factor parameters.
3. The vegetation index based real-time image leaf segmentation system of claim 1 wherein the VDMA module includes an S2MM channel and an MM2S channel therein;
The S2MM channel is used for providing a channel for directly accessing the memory to cache the real-time processing result;
and the MM2S channel is used for providing a path for storing the cached processing result to an external memory card.
4. The vegetation index based real-time image vane segmentation system of claim 1, further comprising an HDMI decoder and HDMI encoder;
The HDMI decoder is respectively connected with an external image acquisition device and the vegetation index calculation module, and is used for receiving the real-time video stream acquired by the external image acquisition device through an HDMI interface, converting the real-time video stream into a VESA standard video stream, and sending the converted video stream to the vegetation index calculation module according to a control instruction of the kernel control module;
the HDMI encoder is used for outputting the processed vegetation index image to the downlink equipment through an HDMI interface.
5. The system for segmenting the real-time image blades based on the vegetation index according to claim 1, wherein the threshold segmentation module sets decision thresholds for three color channels of RGB respectively, performs logical AND operation on areas in the three color channel thresholds to obtain a mask of the position of the blade in the input image, and achieves segmentation of the blade image.
6. The real-time image leaf segmentation system based on a vegetation index according to claim 1, further comprising a filtering module for performing histogram equalization and median filtering processing on the vegetation index image;
the specific processing procedure of the histogram equalization is as follows:
Wherein, H in represents the frequency histogram of one color channel in an input frame of image, and is obtained by counting the gray frequency of the corresponding color channel in the frame of image; l max represents the maximum luminance value; size indicates how many pixels there are in a frame of image; l in is the gray value of a pixel point in the input image; l out represents the processing result pixel value of the corresponding pixel after the histogram equalization processing;
Respectively carrying out histogram equalization on three RGB channels of each frame of image, and combining processing results of the three channels at an output end to form histogram equalization of a vegetation index group;
the specific processing procedure of the median filtering processing is as follows:
Wherein, P1, P2, …, P9 are adjacent 3*3 pixel matrixes; max1, max2, max3 respectively refer to the maximum values of the first, second, and third rows in 3*3 pixel matrices; mid1, mid2, mid3 refer to the intermediate values of the first, second, and third rows in the 3*3 pixel matrix, respectively; min1, min2 and Min3 refer to the minimum values of the first, second and third rows in the 3*3 pixel matrix respectively; max_min is the minimum value of Max1, max2 and Max 3; max_mid is the intermediate value among Mid1, mid2, mid3 described above; min_max is the minimum value of Max1, max2 and Max 3; the median filtering results are the median of max_min, mid_mid, and min_max.
7. A real-time image leaf segmentation method based on a vegetation index, which is applied to the real-time image leaf segmentation system as set forth in any one of claims 1 to 6, and comprises the following steps:
Receiving a control instruction, a video stream and a scaling factor parameter, calculating each vegetation index according to the control instruction and combining the scaling factor parameter, dividing each vegetation index into a group, respectively writing each vegetation index into three channels of red R, green G and blue B, and generating an RGB color video stream as a vegetation index image to output;
Receiving the vegetation index image and caching the vegetation index image according to the control instruction;
and carrying out leaf segmentation on the vegetation index image according to a preset judgment threshold value.
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Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109583378A (en) * | 2018-11-30 | 2019-04-05 | 东北大学 | A kind of vegetation coverage extracting method and system |
| CN110717852A (en) * | 2019-06-13 | 2020-01-21 | 内蒙古大学 | FPGA-based field video image real-time segmentation system and method |
| CN110889394A (en) * | 2019-12-11 | 2020-03-17 | 安徽大学 | Rice lodging recognition method based on deep learning UNet network |
| AU2020100917A4 (en) * | 2020-06-02 | 2020-07-09 | Guizhou Institute Of Pratacultural | A Method For Extracting Vegetation Information From Aerial Photographs Of Synergistic Remote Sensing Images |
-
2021
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109583378A (en) * | 2018-11-30 | 2019-04-05 | 东北大学 | A kind of vegetation coverage extracting method and system |
| CN110717852A (en) * | 2019-06-13 | 2020-01-21 | 内蒙古大学 | FPGA-based field video image real-time segmentation system and method |
| CN110889394A (en) * | 2019-12-11 | 2020-03-17 | 安徽大学 | Rice lodging recognition method based on deep learning UNet network |
| AU2020100917A4 (en) * | 2020-06-02 | 2020-07-09 | Guizhou Institute Of Pratacultural | A Method For Extracting Vegetation Information From Aerial Photographs Of Synergistic Remote Sensing Images |
Non-Patent Citations (1)
| Title |
|---|
| 基于低空可见光谱的植被覆盖率计算;卞雪等;《水土保持通报》;20171015(第05期);全文 * |
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