Zero-Shot Recognition of Test Tube Types by Automatically Collecting and Labeling RGB Data

Abstract

This work presents a method for automatically detecting and recognizing test tube types in a rack. It leverages automatic segmentation, clustering, and labeling processes to eliminate the need for explicitly preparing training data. These processes are addressed by using combined global prediction and local cropping, where global prediction estimates the slot occupation states of a rack, and local cropping extracts tube pictures in the local regions of each slot for clustering and labeling. With the help of the proposed method, the robotic tube manipulation system no longer needs tailored data and explicit training in the presence of new tubes, thus achieving flexibility and efficiency. Experimental evaluations conducted with a RealSense D405 camera and the UFactory xArm Lite6 robot manipulator confirm the method's effectiveness in accurately identifying novel test tube types under real-world conditions.