Deep Learning-Based Semantic Segmentation of Microscale Objects

Abstract

Accurate estimation of the positions and shapes of microscale objects is crucial for automated imaging-guided manipulation using a non-contact technique such as optical tweezers. Perception methods that use traditional computer vision algorithms tend to fail when the manipulation environments are crowded. In this paper, we present a deep learning model for semantic segmentation of the images representing such environments. Our model successfully performs segmentation with a high mean Intersection Over Union score of 0.91.

A condensed version of the paper is published in the Proceedings of the 2019 International Conference on Manipulation, Automation and Robotics at Small Scales