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. 2022 May 4:13:849821.
doi: 10.3389/fpls.2022.849821. eCollection 2022.

A Shape Reconstruction and Measurement Method for Spherical Hedges Using Binocular Vision

Yawei Zhang  1 , Jin Gu  1 , Tao Rao  1 , Hanrong Lai  1 , Bin Zhang  1 , Jianfei Zhang  2 , Yanxin Yin  3   4
Affiliations

A Shape Reconstruction and Measurement Method for Spherical Hedges Using Binocular Vision

Yawei Zhang et al. Front Plant Sci. .

Abstract

The center coordinate and radius of the spherical hedges are the basic phenotypic features for automatic pruning. A binocular vision-based shape reconstruction and measurement system for front-end vision information gaining are built in this paper. Parallel binocular cameras are used as the detectors. The 2D coordinate sequence of target spherical hedges is obtained by region segmentation and object extraction process. Then, a stereo correcting algorithm is conducted to keep two cameras to be parallel. Also, an improved semi-global block matching (SGBM) algorithm is studied to get a disparity map. According to the disparity map and parallel structure of the binocular vision system, the 3D point cloud of the target is obtained. Based on this, the center coordinate and radius of the spherical hedges can be measured. Laboratory and outdoor tests on shape reconstruction and measurement are conducted. In the detection range of 2,000-2,600 mm, laboratory test shows that the average error and average relative error of standard spherical hedges radius are 1.58 mm and 0.53%, respectively; the average location deviation of the center coordinate of spherical hedges is 15.92 mm. The outdoor test shows that the average error and average relative error of spherical hedges radius by the proposed system are 4.02 mm and 0.44%, respectively; the average location deviation of the center coordinate of spherical hedges is 18.29 mm. This study provides important technical support for phenotypic feature detection in the study of automatic trimming.

Keywords: 3D point cloud; binocular vision; dimension measurement; shape reconstruction; spherical hedges.

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Conflict of interest statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Figures

FIGURE 1
FIGURE 1
Schematic diagram of the binocular vision system.
FIGURE 2
FIGURE 2
Flow chart of the measurement system.
FIGURE 3
FIGURE 3
Schematic diagram of pinhole imaging.
FIGURE 4
FIGURE 4
The parallel structure of the binocular vision system.
FIGURE 5
FIGURE 5
The Image-1 and Image-2.
FIGURE 6
FIGURE 6
RGB color histogram of Image-2.
FIGURE 7
FIGURE 7
(A) The gray image of Image-2 obtained after the Ultra-green algorithm. (B) The bilateral filtered image of Image-2.
FIGURE 8
FIGURE 8
The gamma transform graph at different γ values.
FIGURE 9
FIGURE 9
The enhanced contrast results of spherical hedge images in weak light and strong light.
FIGURE 10
FIGURE 10
The Image-2 after binarization.
FIGURE 11
FIGURE 11
Schematic diagram of Bouguet’s algorithm.
FIGURE 12
FIGURE 12
Flow chart of the improved SGBM algorithm.
FIGURE 13
FIGURE 13
Corner extraction results.
FIGURE 14
FIGURE 14
Binocular calibration errors of each image pairs.
FIGURE 15
FIGURE 15
The position and attitude relationship between cameras and calibration chessboard.
FIGURE 16
FIGURE 16
Comparison of before and after stereo correction.
FIGURE 17
FIGURE 17
Schematic diagram of laboratory test.
FIGURE 18
FIGURE 18
3D reconstruction result of outdoor test.
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