Algorithm for Extracting the 3D Pose Information of Hyphantria cunea (Drury) with Monocular Vision

文献类型： 外文期刊

第一作者： Chen, Meixiang

作者： Chen, Meixiang;Zhang, Ruirui;Han, Meng;Yi, Tongchuan;Chen, Meixiang;Zhang, Ruirui;Han, Meng;Yi, Tongchuan;Xu, Gang;Chen, Liping;Chen, Meixiang;Zhang, Ruirui;Han, Meng;Yi, Tongchuan;Xu, Gang;Chen, Liping;Ren, Lili

作者机构：

关键词： monocular vision; Hyphantria cunea (Drury); 3D posture; edge fitting; stereo matching

期刊名称：AGRICULTURE-BASEL （ 影响因子：3.408； 五年影响因子：3.459 ）

ISSN：

年卷期： 2022 年 12 卷 4 期

页码：

收录情况： SCI

摘要： Currently, the robustness of pest recognition algorithms based on sample augmentation with two-dimensional images is negatively affected by moth pests with different postures. Obtaining three-dimensional (3D) posture information of pests can provide information for 3D model deformation and generate training samples for deep learning models. In this study, an algorithm of the 3D posture information extraction method for Hyphantria cunea (Drury) based on monocular vision is proposed. Four images of every collected sample of H. cunea were taken at 90 degrees intervals. The 3D pose information of the wings was extracted using boundary tracking, edge fitting, precise positioning and matching, and calculation. The 3D posture information of the torso was obtained by edge extraction and curve fitting. Finally, the 3D posture information of the wings and abdomen obtained by this method was compared with that obtained by Metrology-grade 3D scanner measurement. The results showed that the relative error of the wing angle was between 0.32% and 3.03%, the root mean square error was 1.9363, and the average relative error of the torso was 2.77%. The 3D posture information of H. cunea can provide important data support for sample augmentation and species identification of moth pests.

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