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

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.