Panoramic looking-around method of corn harvester based on dynamic and static fusion

Abstract

The invention discloses a corn harvester panoramic looking-around method based on dynamic and static fusion, and belongs to the field of image processing and computer vision, and the method comprises the following steps: respectively installing fisheye cameras on a front vehicle body and a rear vehicle body of a corn harvester, and installing an angle sensor device at a hinge point of the front vehicle body and the rear vehicle body, the front vehicle body is a part with a vehicle shovel, and the rear vehicle body is a part far away from the vehicle shovel; acquiring a rotation angle of the front vehicle body relative to the rear vehicle body based on the angle sensor device; calibrating a homography matrix of aerial view transformation of the camera based on the calibration cloth; splicing the aerial view images obtained by the fisheye camera based on the homography matrix to obtain three-side look-around images of a front vehicle body and a rear vehicle body; and dynamically fusing the three-side look-around images of the front and rear vehicle bodies by adopting the rotation angle of the front vehicle body relative to the rear vehicle body to obtain a panoramic look-around image of the corn harvester.

Description

Technical Field

[0001] The present invention belongs to the technical field of image processing and computer vision, and in particular relates to a panoramic viewing method for a corn harvester based on dynamic and static fusion. Background Art

[0002] With the advancement of modern agricultural mechanization, corn harvesters, as a key piece of agricultural equipment, play a particularly important role in corn harvesting. However, traditional corn harvesters, due to their large size and complex operating environment, have limited vision and large blind spots. This can be particularly prone to accidents when turning, approaching obstacles, or working around other operators. Therefore, improving the safety and efficiency of corn harvesters has become a key area of technological development.

[0003] Although some surround-view systems have been used in agricultural machinery to help drivers observe the vehicle's surroundings, most systems suffer from the following issues: First, the camera's field of view is limited and cannot fully cover the vehicle's perimeter. Second, dynamic changes in the vehicle's motion (such as turning and vibration) lead to errors in image splicing and fusion, making it difficult to provide a clear, seamless panoramic view. Third, existing systems have weak dynamic adjustment capabilities for different angles and are unable to adapt to changes in the vehicle's posture during driving in real time, resulting in poor system stability and accuracy. Therefore, to address the problem of insufficient field of view for large corn harvesters during operation, the present invention proposes a panoramic surround-view method for corn harvesters based on dynamic and static fusion. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention proposes a panoramic view method for a corn harvester based on dynamic and static fusion to solve the problems existing in the above-mentioned prior art.

[0005] To achieve the above objectives, the present invention provides a panoramic view method for a corn harvester based on dynamic and static fusion, comprising:

[0006] Fisheye cameras are installed on the front and rear bodies of the corn harvester, and angle sensors are installed at the hinge points of the front and rear bodies, wherein the front body is the part with the shovel and the rear body is the part away from the shovel;

[0007] obtaining a rotation angle of the front vehicle body relative to the rear vehicle body based on the angle sensor device;

[0008] The homography matrix of the bird's-eye view transformation of the camera is calibrated based on the calibration cloth;

[0009] The three-side surround view images of the front and rear vehicle bodies are obtained by stitching the bird's-eye view images obtained by the fisheye camera based on the homography matrix;

[0010] The three-side surround view images of the front and rear bodies are dynamically fused using the rotation angle of the front body relative to the rear body to obtain a panoramic surround view image of the corn harvester.

[0011] Optionally, before fisheye cameras are respectively installed on the front body and the rear body of the corn harvester, the method further includes: performing distortion correction on the fisheye cameras using a Kannala-Brandt model.

[0012] Optionally, the process of installing fisheye cameras on the front and rear bodies of the corn harvester includes:

[0013] The front body and the rear body are connected via an angle sensor device; wherein the connection end between the front body and the rear body is the other side corresponding to the front side of the front body;

[0014] Fisheye cameras are installed on the front, left, and right sides of the front vehicle body respectively; the front side of the front vehicle body is the side connected to the shovel;

[0015] Fisheye cameras are installed on the rear side, left side and right side of the rear body respectively; wherein, the rear side of the rear body is the side corresponding to the connection end with the front body.

[0016] Optionally, the process of calibrating the homography matrix of the bird's-eye view transformation of the camera based on the calibration cloth includes: performing a projective transformation to solve the homography matrix based on the image coordinates and ground coordinates of the corner points in the calibration cloth;

[0017] The expression of the projection transformation is:

[0018] sP g =HP i

[0019] Where s is the scaling factor, P g represents the ground coordinates of the corner points, H represents the homography matrix, P i The image coordinates representing the corner points.

[0020] Optionally, the process of stitching the bird's-eye view images obtained by the fisheye camera to obtain three-side surround view images of the front and rear vehicle bodies includes:

[0021] The static fusion method is used to perform weighted fusion on the left image obtained by the front fisheye camera and the front image obtained by the left fisheye camera to obtain the overlapping area of the left front corner of the front vehicle body;

[0022] The static fusion method is used to perform weighted fusion on the right side image obtained by the front fisheye camera and the front side image obtained by the right fisheye camera to obtain the overlapping area of the right front corner of the front vehicle body;

[0023] The static fusion method is used to perform weighted fusion on the left image obtained by the rear fisheye camera and the rear image obtained by the left fisheye camera to obtain the overlapping area of the rear left corner of the rear body;

[0024] The static fusion method is used to perform weighted fusion on the right side image obtained by the rear fisheye camera and the rear side image obtained by the right fisheye camera to obtain the overlapping area of the right rear corner of the rear vehicle body;

[0025] A three-side surround view image of the front and rear vehicles is obtained based on the front left corner overlap area, the front right front corner overlap area, the rear left rear corner overlap area and the rear right rear corner overlap area.

[0026] Optionally, the calculation expression of the weight in weighted fusion of the left image obtained by the front fisheye camera and the front image obtained by the left fisheye camera of the front vehicle body is:

[0027]

[0028] Where w (u,v) represents the weight of the left image obtained by the front fisheye camera, (u, v) represents the pixels of the left image obtained by the front fisheye camera, w is the width of the overlapping area, and h is its length.

[0029] Optionally, the process of dynamically fusing the three-side surround view images of the front and rear bodies using the rotation angle of the front body relative to the rear body to obtain the panoramic surround view image of the corn harvester includes:

[0030] Rotating the three-side surround view images of the front vehicle body around the hinge point to obtain a rotation angle of the front vehicle body relative to the rear vehicle body to obtain a rotated three-side surround view of the front vehicle body;

[0031] The three-side surround view image of the rotated front vehicle body and the three-side surround view image of the rear vehicle body are dynamically fused to obtain a panoramic surround view image of the corn harvester.

[0032] Optionally, the weight calculation expression during dynamic fusion is:

[0033]

[0034] Where w (x,y) Indicates the weight of the pixel coordinates between the upper and lower limits when the three-side surround view image of the front vehicle body is rotated left and right. x represents the horizontal coordinate of the pixel point, y represents the vertical coordinate of the pixel point, and v u Indicates the upper limit of the final overlap area of the three-side surround view images of the front and rear vehicle bodies, v d Indicates the lower limit of the final overlap area of the three-side surround view images of the front and rear vehicle bodies.

[0035] Compared with the prior art, the present invention has the following advantages and technical effects:

[0036] This patent proposes a method for panoramic surround view of a corn harvester based on dynamic and static fusion. By installing fisheye cameras and angle sensor devices on the front and rear bodies of the corn harvester, the rotation angle of the body is obtained, and the bird's-eye view images are spliced based on the homography matrix to achieve dynamic fusion of the three-side surround view images of the front and rear bodies, and finally generate a panoramic surround view image. This method can effectively solve the blind spot problem of the articulated parts of the corn harvester body, provide the driver with a more comprehensive field of view, and improve the safety and efficiency of the operation. At the same time, the static fusion method is used to process the overlapping area images, combined with dynamic fusion technology to ensure the accuracy and real-time performance of image splicing, further enhancing the reliability and practicality of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings, which constitute part of this application, are intended to provide a further understanding of this application. The exemplary embodiments and descriptions of this application are intended to explain this application and do not constitute an improper limitation on this application. In the accompanying drawings:

[0038] Figure 1 This is a picture of a vehicle-mounted fisheye camera according to an embodiment of the present invention;

[0039] Figure 2 A diagram of an angle sensor device according to an embodiment of the present invention;

[0040] Figure 3 A flowchart of panoramic image generation according to an embodiment of the present invention;

[0041] Figure 4 This is a schematic diagram of equipment installation and calibration cloth laying according to an embodiment of the present invention;

[0042] Figure 5 This is a diagram showing the distributed display experiment results of a panoramic surround view system according to an embodiment of the present invention;

[0043] Figure 6 This is a bird's-eye view experimental result diagram of the panoramic view system of a corn harvester according to an embodiment of the present invention. DETAILED DESCRIPTION

[0044] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0045] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0046] Example 1

[0047] The present invention provides a panoramic surround view system based on dynamic and static fusion, designed to address blind spots and provide enhanced safety assistance for drivers. The system captures surrounding images by installing fisheye cameras on three sides of the front and rear bodies of a corn harvester. An angle sensor device installed at the hinge of the front and rear bodies captures the rotation angle of the front body relative to the rear body. The camera images are collected by a recorder and transmitted to a development board. The angle sensor and the development board communicate via serial ports for data transmission. After processing using projection transformation and dynamic and static fusion methods, the system can obtain a panoramic surround view image of the corn harvester.

[0048] The angle sensor assembly consists of an angle sensor, a connecting rod assembly, and a fixed block at the end of the connecting rod. The connecting rod assembly is fixedly connected to the angle sensor hub, and the fixed block is connected to the connecting rod through a hole in the connecting rod. At the front and rear body hinge, the connecting rod assembly is welded to the front body, and the fixed block is welded to the rear body. This ensures that the hinge point is below the center of the connecting rod when the front and rear bodies are parallel. Rotation of the front body causes the angle sensor to rotate relative to the fixed block on the rear body.

[0049] The specific implementation process includes:

[0050] Step 1: Calibrate the intrinsic parameter matrix and distortion parameters of the fisheye camera to prepare for subsequent distortion correction.

[0051] Step 2: Install fisheye cameras on three sides of the front and rear vehicle bodies, ensuring sufficient field of view and overlap between adjacent cameras. Angle sensors are installed at the front and rear vehicle body hinges.

[0052] Step 3: Park the corn harvester in a pre-designated area and place two calibration cloths at pre-designed locations on the three sides of the front and rear bodies. Use the corner points of the calibration cloths to calibrate the bird's-eye view transformation homography matrices of the six fisheye cameras.

[0053] Step 4: Connect the development board to the recorder to capture images from the six cameras. Distortion correction and projection transformation are performed, and the bird's-eye view images of the front and rear vehicle sides are stitched together. Static fusion is performed on the four overlapping areas (the upper left and right corners of the front vehicle and the lower left and right corners of the rear vehicle) to obtain surround view images of the front and rear vehicle sides.

[0054] Step 5: First, read the angle sensor data through the serial port and calculate the rotation angle of the front vehicle body relative to the rear vehicle body. Then, rotate the surround view of the front vehicle body. Then, dynamically fuse the overlapping parts of the front and rear surround views to finally obtain a panoramic surround view image of the corn harvester.

[0055] As a specific implementation of this embodiment, use Figure 1 The car-mounted fisheye camera shown in the figure, Figure 2The angle sensor device, car driving recorder, and development board shown in the figure build a system that collects images using fisheye cameras installed on three sides of the front and rear body. Figure 5 As shown in the figure, the angle sensor device installed at the hinge is used to obtain the rotation angle of the front vehicle body relative to the rear vehicle body, and a panoramic image around the corn harvester is obtained based on dynamic and static fusion, generating a flow chart as shown in the figure. Figure 3 The steps are as follows:

[0056] Step 1: Use the Kannala-Brandt model to correct fisheye camera distortion. The specific process is as follows:

[0057] s1: world coordinates to camera coordinates.

[0058] World coordinate point P w =(X w ,Y w ,Z w ) is converted to point P in the camera coordinate system c :

[0059] P c =RP w +t

[0060] Where R is the rotation matrix, t is the translation vector, P c =(X c ,Y c ,Z c ).

[0061] s2: Normalized projection.

[0062] Project the camera coordinates onto the normalized plane (assuming the optical center is at the origin):

[0063]

[0064] Among them, P n =(x n ,y n ) are its coordinate points on the normalized plane.

[0065] s3: Calculate the incident angle θ

[0066] Polar coordinate form of normalized coordinates:

[0067]

[0068] where r n is the polar diameter, θ is the polar angle

[0069] s4: Apply the distortion model (Kannala-Brandt).

[0070] Calculate the distorted radius r using the polynomial model d :

[0071] r d =θ(k0+k1θ 2 +k2θ 4 +k3θ 6 +k4θ 8 )

[0072] Where k0, k1, k2, k3, and k4 are distortion coefficients.

[0073] s5: Calculate the normalized coordinates after distortion (x d ,y d ):

[0074]

[0075] s6: Pixel coordinate conversion.

[0076] The original images of the calibration plate at various angles taken by multiple fisheye cameras are calibrated through Opencv related modules to obtain the fisheye camera intrinsic parameter matrix K and distortion parameter D, which are converted to pixel coordinates through the intrinsic parameter matrix:

[0077]

[0078] Where (u, v) is the image pixel coordinate, the intrinsic parameter matrix K and the distortion parameter D are defined as:

[0079]

[0080] where f x is the focal length in the x-axis direction of the image plane, f y is the length of the focal length in the y-axis direction, (c x ,c y ) are the main point coordinates.

[0081] s7: Distortion correction process.

[0082] Distortion correction is the inverse process of the above process, that is, to infer the undistorted normalized coordinate P from the distorted pixel coordinates (u, v) n Due to the nonlinearity of the model, an iterative or approximate solution is usually required.

[0083] First, the distortion normalized coordinates are inferred through the internal parameter matrix:

[0084]

[0085] Then calculate the distortion radius:

[0086]

[0087] Then the incident angle θ is solved inversely using the polynomial.

[0088] Finally, restore the undistorted normalized coordinates:

[0089]

[0090] Step 2: After obtaining the camera intrinsic parameters and distortion parameters in step 1, distortion correction can be performed after obtaining the camera's fisheye original image. Press Figure 4 As shown in the figure, six fisheye cameras and angle sensor devices are installed, the corn harvester is parked in the designated area, and two calibration cloths are used to calibrate the homography matrices of the bird's-eye view transformation of the six cameras. Specifically, Figure 4 As shown:

[0091] Place the calibration cloth on the side of the vehicle body. Figure 4 In the area 1-8 marked by the numbers in the middle shaded area, use the fisheye camera on the side of the vehicle to obtain images and perform distortion correction to obtain the coordinates P of the four corner points on the periphery of the calibration cloth on the image. i , the coordinates of the corner points on the ground are pre-designed as P g , the homography matrix is H, and the projection transformation is:

[0092] sP g =HP i

[0093] Where s is the scaling factor, P i and P g In homogeneous form:

[0094]

[0095] Using 4 pairs of points, H can be solved as follows:

[0096] Expanding and eliminating the scaling factor s, we get two linear equations:

[0097]

[0098] For 4 pairs of points, a total of 8 equations are constructed and combined into a coefficient matrix A (8×9). Solve the homogeneous equation Ah=0. After solving H, divide it by the last element of the matrix, even if h 33 to 1 to eliminate the scale degree of freedom.

[0099] Step 3: After obtaining the homography transformation matrices of the three sides of the front and rear vehicle bodies in step 2, perform a projection transformation on the distortion-corrected image to obtain a bird's-eye view image. First, splice the bird's-eye view images of the three sides of the front and rear vehicle bodies. For areas that do not overlap between a certain side camera and other side cameras, retain the bird's-eye view image of the side camera. Then, image fusion is performed on the four-corner overlapping area. Here, a static fusion method is used. Taking the overlapping area of the left front corner of the front vehicle body as an example, the square overlapping area is formed by weighted fusion of the left image of the front camera and the front image of the left camera. The pixel coordinates of the left image of the front camera are (u, v), and its weight is w (u,v) , while the weight of the same pixel position in the front image of the left camera is 1-w (u,v) , the specific weight calculation formula is as follows:

[0100]

[0101] Where w is the width of the square overlap area and h is its length.

[0102] In this way, a weighted coefficient matrix can be generated with the weights gradually changing from 1 to 0 along the diagonal direction from the upper right corner to the lower left corner, ensuring that the front image is dominant when the overlapping area is close to the front, and the left image is dominant when it is close to the left. Other triangular overlapping areas are fused in this way.

[0103] Step 4: Read the angle sensor reading as α. If the angle sensor rotates clockwise, α is positive, and if it rotates counterclockwise, α is negative. According to the geometric relationship, the rotation angle θ of the front vehicle body relative to the rear vehicle body is (right turn is positive):

[0104] θ=-2α

[0105] Then, the bird's-eye view images of the three sides of the front vehicle body are rotated by θ with the hinge point as the center. The coordinates of all pixels after the rotation by θ are calculated. Assuming that the coordinates before the rotation are (u1, v1), the coordinates after the rotation are (u2, v2), and the coordinates of the hinge point are (u0, v0), the relationship is:

[0106]

[0107] In actual use, the inverse relationship, i.e. (u2, v2) → (u1, v1), is used to map the pixel values according to the coordinate transformation relationship. However, since the pixel coordinates are all integer values, bilinear interpolation is used here, otherwise there will be regularly arranged blank pixels, as shown below:

[0108] After inferring (u1, v1) from (u2, v2), the pixel value of (u2, v2) is assigned based on the surrounding pixels of (u1, v1). Here, (u2, v2) is an integer, but (u1, v1) is generally not. Rounding down and rounding up (u1, v1) yields the pixel coordinates of the upper left corner as (ulu ,v lu ), the upper right pixel coordinate is (u ru ,v ru ), the pixel coordinates of the lower left corner are (u ld ,v ld ), the pixel coordinates of the lower right corner are (u rd ,v rd ), the corresponding pixel values are q 11 ,q 12 ,q 21 ,q 22 If it exceeds the image boundary, return black pixels to avoid crossing the boundary. The decimal part of (u1,v1) is recorded as u frac and v frac , and then perform bilinear interpolation on the three channels:

[0109] First perform linear interpolation in the horizontal direction:

[0110] Interpolation of the top left and top right corners:

[0111] q1[i]=(1-u frac )q 11 [i]+u frac q 21 [i]

[0112] Interpolation of the lower left and lower right corners:

[0113] q2[i]=(1-u frac )q 12 [i]+u frac q 22 [i]

[0114] Then do linear interpolation in the vertical direction:

[0115] q r [i]=(1-v frac )q1[i]+v frac q2[i]

[0116] Where [i] represents one of the three channels, and the final interpolated pixel value is q r .

[0117] Step 5: After rotating the front three-sided view, the non-overlapping areas remain unchanged. The rotated image is then fused with the overlapping areas of the rear three-sided view. This process uses a dynamic fusion method to calculate the weighted fusion coefficient in real time:

[0118] Assuming that the upper limit of the overlap area when the designed rotation angle is 0 is The lower limit is Calculate the vertical coordinate values of the lower left and lower right corners of the front vehicle body view after rotation, and the upper limit of the final overlapping area is v u , the lower limit is v d , when turning left, the vertical coordinate of the lower left corner after rotation is The vertical coordinate of the lower right corner after rotation is at this time:

[0119]

[0120] When turning right, the vertical coordinate of the lower left corner point after rotation is The vertical coordinate of the lower right corner after rotation is at this time:

[0121]

[0122] The pixel coordinates between the upper and lower limits are (x, y), and the weight of the point in the front vehicle surround view is w (x,y) , the weight of this point in the rear body surround view is 1-w (x,y) , the weight calculation formula is as follows:

[0123]

[0124] Where w (x,y) Indicates the weight of the pixel coordinates between the upper and lower limits when the three-side surround view image of the front vehicle body is rotated left and right. x represents the horizontal coordinate of the pixel point, y represents the vertical coordinate of the pixel point, and v u Indicates the upper limit of the final overlap area of the three-side surround view images of the front and rear vehicle bodies, v d Indicates the lower limit of the final overlap area of the three-side surround view images of the front and rear vehicle bodies.

[0125] In this way, a panoramic view of the corn harvester can be obtained in real time. Figure 6 shown.

[0126] The above are merely preferred embodiments of the present application, but the scope of protection of the present application is not limited thereto. Any changes or substitutions that can be easily conceived by a person skilled in the art within the technical scope disclosed in this application should be included in the scope of protection of the present application. Therefore, the scope of protection of the present application should be based on the scope of protection of the claims.

Claims

1. A panoramic view method for corn harvester based on dynamic and static fusion, characterized in that: The following steps are involved: Fisheye cameras are installed on the front and rear bodies of the corn harvester, and angle sensors are installed at the hinge points of the front and rear bodies, wherein the front body is the part with the shovel and the rear body is the part away from the shovel; obtaining a rotation angle of the front vehicle body relative to the rear vehicle body based on the angle sensor device; The homography matrix of the bird's-eye view transformation of the camera is calibrated based on the calibration cloth; The three-side surround view images of the front and rear vehicle bodies are obtained by stitching the bird's-eye view images obtained by the fisheye camera based on the homography matrix; The three-side surround view images of the front and rear bodies are dynamically fused using the rotation angle of the front body relative to the rear body to obtain a panoramic surround view image of the corn harvester.

2. The method according to claim 1, characterized in that Before fisheye cameras are respectively installed on the front body and the rear body of the corn harvester, the method further includes: using a Kannala-Brandt model to correct distortion of the fisheye cameras.

3. The method according to claim 1, characterized in that The process of installing fisheye cameras on the front and rear bodies of a corn harvester includes: The front body and the rear body are connected via an angle sensor device; wherein the connection end between the front body and the rear body is the other side corresponding to the front side of the front body; Fisheye cameras are installed on the front, left, and right sides of the front vehicle body respectively; the front side of the front vehicle body is the side connected to the shovel; Fisheye cameras are installed on the rear side, left side and right side of the rear body respectively; wherein, the rear side of the rear body is the side corresponding to the connection end with the front body.

4. The method according to claim 3, characterized in that The process of calibrating the homography matrix of the bird's-eye view transformation of the camera based on the calibration cloth includes: performing a projection transformation based on the image coordinates and ground coordinates of the corner points in the calibration cloth to solve the homography matrix; The expression of the projection transformation is: sP g =HP i Where s is the scaling factor, P g represents the ground coordinates of the corner points, H represents the homography matrix, P i The image coordinates representing the corner points.

5. The method according to claim 3, characterized in that The process of stitching the bird's-eye view images obtained by the fisheye camera to obtain three-side surround view images of the front and rear vehicle bodies includes: The static fusion method is used to perform weighted fusion on the left image obtained by the front fisheye camera and the front image obtained by the left fisheye camera to obtain the overlapping area of the left front corner of the front vehicle body; The static fusion method is used to perform weighted fusion on the right side image obtained by the front fisheye camera and the front side image obtained by the right fisheye camera to obtain the overlapping area of the right front corner of the front vehicle body; The static fusion method is used to perform weighted fusion on the left image obtained by the rear fisheye camera and the rear image obtained by the left fisheye camera to obtain the overlapping area of the rear left corner of the rear body; The static fusion method is used to perform weighted fusion on the right side image obtained by the rear fisheye camera and the rear side image obtained by the right fisheye camera to obtain the overlapping area of the right rear corner of the rear vehicle body; A three-side surround view image of the front and rear vehicles is obtained based on the front left corner overlap area, the front right front corner overlap area, the rear left rear corner overlap area and the rear right rear corner overlap area.

6. The method according to claim 5, characterized in that The calculation expression of the weight in the weighted fusion of the left image obtained by the front fisheye camera and the front image obtained by the left fisheye camera of the front vehicle body is: Where w (u,v) represents the weight of the left image obtained by the front fisheye camera, (u, v) represents the pixels of the left image obtained by the front fisheye camera, w is the width of the overlapping area, and h is its length.

7. The method according to claim 6, characterized in that The process of dynamically fusing the three-side surround view images of the front and rear bodies using the rotation angle of the front body relative to the rear body to obtain a panoramic surround view image of the corn harvester includes: Rotating the three-side surround view images of the front vehicle body around the hinge point to obtain a rotation angle of the front vehicle body relative to the rear vehicle body to obtain a rotated three-side surround view of the front vehicle body; The three-side surround view image of the rotated front vehicle body and the three-side surround view image of the rear vehicle body are dynamically fused to obtain a panoramic surround view image of the corn harvester.

8. The method according to claim 7, characterized in that The weight calculation expression during dynamic fusion is: Where w (x,y) Indicates the weight of the pixel coordinates between the upper and lower limits when the three-side surround view image of the front vehicle body is rotated left and right. x represents the horizontal coordinate of the pixel point, y represents the vertical coordinate of the pixel point, and v u Indicates the upper limit of the final overlap area of the three-side surround view images of the front and rear vehicle bodies, v d Indicates the lower limit of the final overlap area of the three-side surround view images of the front and rear vehicle bodies.

Images