FASTDLO: Fast Deformable Linear Objects Instance Segmentation
IEEE Robotics and Automation Letters(2022)
摘要
In this paper, an approach for fast and accurate segmentation of Deformable Linear Objects (DLOs) named
FASTDLO
is presented. A deep convolutional neural network is employed for background segmentation, generating a binary mask that isolates DLOs in the image. Thereafter, the obtained mask is processed with a skeletonization algorithm and the intersections between different DLOs are solved with a similarity-based network. Apart from the usual pixel-wise color-mapped image,
FASTDLO
also describes each DLO instance with a sequence of 2D coordinates, enabling the possibility of modeling the DLO instances with splines curves, for example. Synthetically generated data are exploited for the training of the data-driven methods, avoiding expensive collection and annotations of real data.
FASTDLO
is experimentally compared against both a DLO-specific approach and general-purpose deep learning instance segmentation models, achieving better overall performances and a processing rate higher than 20 FPS.
更多查看译文
关键词
Deformable Linear Objects,DLO,Instance Segmentation,Industrial Manufacturing,Computer Vision
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要