Generating synthetic dataset for scale-invariant instance segmentation of food materials based upon mask r-cnn - Min, Hyeun Jeong (Journal of Institute of Control, Robotics and Systems, 2021)
This work proposes a scale-invariant instance segmentation method for images acquired from a real-time camera. It is challenging to detect and segment an exact shape by removing background (named as an instance) of a deformable semi-solid object such as food materials. In this work, we consider the segmentation with the cases of various sizes of an object and multiple objects overlapped with each other. To do this, we address an augmented dataset generation method, which extends dataset from small number of base objects, a fundamental dataset. Our method is based upon data augmentation, which is well known that it is an effective way to improve the segmentation performance. Our method addresses the generation of dataset with various scales using small number of original dataset. It is relatively simple in method but provides better performance. We also propose how to choose a target object (food material) with its centroid for grasping. Through diverse experiments using real-time images, we demonstrate that the proposed algorithm segments scale-invariant object maskss and is successfully implemented for a robotic hand to grasp a food material. It is also compared with the state-of-the-art segmentation algorithm. As a result, the proposed method shows 74%, 85%, and 78% in accuracy, recall, and precision while the original datasett shows 67%, 79%, and 70%, respectively.
Keywords: Data augmentation; Generation method; Multiple objects; Real time images; Segmentation algorithms; Segmentation methods; Segmentation performance; State of the art; Food materials; Instance segmentation; Scale-invariant; Synthetic dataset