Design of a laboratory component warehousing system based on machine vision
Aiming at the problems of low efficiency of manual management of electronic components in university laboratories, error-prone inventory, and difficult control of small components, this paper designs an automatic sorting and storage system based on machine vision and embedded control. The system uses Raspberry PI 5B as the main control and K230 as the visual processing unit, uses YOLO11n(You Only Look Once) lightweight algorithm to realize real-time and highprecision detection of 10 types of components, and completes automatic grasping, transportation and classified storage through a three-axis closed-loop manipulator and flexible gripper. Alibaba Cloud IoT(Internet of Things) platform and HarmonyOS mobile APP were used to realize remote monitoring of inventory data. Test results indicate that the system achieves an average recognition accuracy of over 90% for 10 categories of target components, with a 100% success rate in capturing. It can reliably complete the full process of automation from identification to warehousing. This solution provides a reusable technical pathway for the engineering application of machine vision in precision sorting and warehouse management, and also serves as a reference example for the integration of vision‑guided automation systems under the theme of the 2026 International Conference on machine Vision, Detection and 3D Imaging Technology (MVDIT 2026).