An intuitive pre-processing method based on human–robot interactions: zero-shot learning semantic segmentation based on synthetic semantic template

In industry, robots are widely used to solve repetitive or dangerous actions in product production, so that product production can be more efficient. However, the problem that robots are often challenged is the convenience and the efficiency of introducing the production line. Therefore, the intuitive robot guidance method is an important issue; this paper will introduce the concept of human–robot interactions (HRI) and use deep learning methods on the machine vision system to complete the robot-guided assembly operation analysis, and the assembly operation analysis requires semantic segmentation as pre-processing. Therefore, we propose a novel semantic template correlation model architecture based on zero-shot learning (ZSL) to achieve the effect of rapid deployment. The semantic template correlation model is to search for the object area offline learning through the semantic template generated by the physics engine, and when inferring online, we can directly enter the semantic template to obtain the relevant object region. Finally, this paper verifies that the MIoU can be increased by more than 5% through the verification of the general database VOC2012.