Intelligent Baijiu Steamer-Filling Distillation System Based on Multimodal Image Fusion

Abstract

This study aims to address the key issue of replacing manual steamer-filling with robotic arms and increasing the yield of baijiu in the traditional Chinese baijiu brewing process through the use of multimodal fusion technology-based intelligent solutions. First, a multimodal dataset of the steamer-filling was established using various sensors, and semantic segmentation annotation and preprocessing were conducted. Subsequently, a multimodal fusion semantic segmentation model was constructed with the aim of perceiving the distiller’s grains laying area during the steamer-filling process by integrating RGB, infrared, and depth modal images, achieving an algorithmic accuracy of 96.8%. Finally, experimental results demonstrate that the proposed intelligent steamer-filling distillation system not only meets the intricate technological requirements of steamer-filling but also increases baijiu production by 8.54% compared with manual methods.