Deep Learning-Based Semantic Segmentation of Microscale Objects
This addresses the challenge of precise object detection in microscale manipulation, which is incremental as it applies deep learning to a known bottleneck in a specific domain.
The paper tackles the problem of accurately segmenting microscale objects in crowded environments for automated imaging-guided manipulation, achieving a mean Intersection Over Union score of 0.91.
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.