Computer Vision for Recognition of Materials and Vessels in Chemistry Lab Settings and the Vector-LabPics Data Set.

ABSTRACT: This work presents a machine learning approach for the computer vision-based recognition of materials inside vessels in the chemistry lab and other settings. In addition, we release a data set associated with the training of the model for further model development. The task to learn is finding the region, boundaries, and category for each material phase and vessel in an image. Handling materials inside mostly transparent containers is the main activity performed by human and robotic chemists in the laboratory. Visual recognition of vessels and their contents is essential for performing this task. Modern machine-vision methods learn recognition tasks by using data sets containing a large number of annotated images. This work presents the Vector-LabPics data set, which consists of 2187 image

SUBMITTER: Eppel S 

PROVIDER: S-EPMC7596871 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature