Research on interactive virtual assembly technology for agricultural machinery components based on OpenPose

In order to enhance the precision and efficiency of the assembly process for intelligent agricultural equipment, this paper integrates machine vision perception and deep learning algorithms, and proposes an OpenPose-based virtual assembly method for key components of equipment. This marks the inaugural instance of employing a binocular stereo vision system in conjunction with the Multiview Bootstrapping algorithm in OpenPose to address the challenges of hand positioning and occlusion during the assembly process. This approach enables the precise detection of hand key points and the accurate calculation of hand position information. To address the high cost of production of physical agricultural equipment, a digital twin model of agricultural equipment components and a virtual hand model are constructed. A virtual assembly platform is then constructed and applied to verify the visual system and conduct interactive virtual assembly tests. The results demonstrate that the platform can rapidly and accurately track the key points of the hand, thereby enabling the interactive virtual assembly of key components of agricultural machinery.