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

Meixiang Chen, Ruirui Zhang, Meng Han, Tongchuan Yi, Gang Xu, Lili Ren and Liping Chen
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Meixiang Chen: National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
Ruirui Zhang: National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
Meng Han: National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
Tongchuan Yi: National Research Center of Intelligent Equipment for Agriculture, Beijing 100097, China
Gang Xu: Research Center for Intelligent Equipment, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China
Lili Ren: Beijing Key Laboratory for Forest Pest Control, Beijing Forestry University, Beijing 100083, China
Liping Chen: Research Center for Intelligent Equipment, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097, China

Agriculture, 2022, vol. 12, issue 4, 1-17

Abstract: 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° 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.

Keywords: monocular vision; Hyphantria cunea (Drury); 3D posture; edge fitting; stereo matching (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2022
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