📃 IEEE Xplore 📃
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.
Main dependencies:
python (3.8)
pytorch (1.4.0)
opencv
pillow
scikit-image
scipy
shapely
Installation (from inside the main project directory):
pip install .
Download the weights and place them inside a weights folder.
import as a standard python package with from fastdlo.core import Pipeline.
Then initialize the class p = Pipeline(checkpoint_siamese_network, checkpoint_segmentation_network)
the inference can be obtained with pred = p.run(source_img) .
This work was supported by the European Commission’s Horizon 2020 Framework Programme with the project REMODEL - Robotic technologies for the manipulation of complex deformable linear objects - under grant agreement No 870133.
DeepLabV3+ implementation based on https://github.com/VainF/DeepLabV3Plus-Pytorch
If you use FASTDLO or this code base in your work, please cite
@ARTICLE{9830852,
author={Caporali, Alessio and Galassi, Kevin and Zanella, Riccardo and Palli, Gianluca},
journal={IEEE Robotics and Automation Letters},
title={FASTDLO: Fast Deformable Linear Objects Instance Segmentation},
year={2022},
volume={7},
number={4},
pages={9075-9082},
doi={10.1109/LRA.2022.3189791}}