Design of intelligent storage cabinet vision system based on machine vision

This thesis focuses on the research and design of the intelligent storage cabinet system for common household items based on the YOLOv5 algorithm. The overall design of the system adopts a hierarchical architecture to ensure modularization and scalability, clarify the core functional requirements, and point out the direction for subsequent development. The part of image processing and recognition algorithms is introduced. The image quality is improved through image preprocessing technology, and the automatic recognition and classification of items are achieved by adopting the object detection and classification method based on deep learning. System testing and optimization test various functions to verify stability and reliability, and propose solutions such as algorithm optimization, hardware upgrade and energy consumption management for actual operation problems to improve overall performance. The research results show that this intelligent storage cabinet system has efficient data collection and processing capabilities as well as a good user interaction experience. It can effectively improve storage efficiency and provide a feasible solution for the field of intelligent storage.