FASTDLO: Fast Deformable Linear Objects Instance Segmentation

Caporali, Alessio; Galassi, Kevin; Zanella, Riccardo; Palli, Gianluca

doi:10.1109/lra.2022.3189791

Cited by 27 publications

(47 citation statements)

References 27 publications

(43 reference statements)

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“…It achieves the instance segmentation of DLOs empowering a segmentation Deep Convolutional Neural Network (DCNN) [14] in its framework. Recently, FASTDLO [3] has been proposed improving the performances of Ariadne+ both in terms of speed and accuracy.…”

Section: A Segmentation Of Dlosmentioning

confidence: 99%

“…For the manipulation of DLOs, the availability of a segmentation mask is fundamental [3]. Therefore, we focus on the impact given by w s and w r in semantic and instance segmentation tasks.…”

Section: Training On a Dataset Mixturementioning

confidence: 99%

“…The baseline deep learning models, datasets mixtures and training pipelines outlined in Sec.III-D are employed to find data regarding RQ2. As real-world test set, a set of 135 manually labeled real images of electrical wires with varying diameters is used from [3]. The IoU is employed as metric, with the mask M corresponding respectively to the binary mask for the semantic segmentation task and to the instance mask where each DLO instance is denoted by a unique color for the instance segmentation task.…”

Section: B Mixing Synthetic and Real Samples For Trainingmentioning

confidence: 99%

“…DLOs are characterized by an elongated cylindrical shape lacking relevant features. Therefore, their detection via Computer Vision (CV) is challenging, and the application of robotics aiming at their autonomous manipulation is heavily affected [1], [2], [3]. In fact, the cabling and wire routing processes, which constitute a huge part of industrial assembly tasks, are still largely performed by humans in the automotive and aerospace sectors [4], [5].…”

Section: Introductionmentioning

confidence: 99%

“…In fact, the cabling and wire routing processes, which constitute a huge part of industrial assembly tasks, are still largely performed by humans in the automotive and aerospace sectors [4], [5]. Consequently, there is a need to propose new perception methodologies based on CV to bridge this gap [2], [3], [6], [7]. Existing data-driven approaches, in real-world applications, are still massively affected by the quality and size of the datasets.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Weakly Supervised Semi-Automatic Image Labeling Approach for Deformable Linear Objects

Caporali

Pantano

Janisch

et al. 2023

IEEE Robot. Autom. Lett.

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The presence of Deformable Linear Objects (DLOs) such as wires, cables or ropes in our everyday life is massive. However, the applicability of robotic solutions to DLOs is still marginal due to the many challenges involved in their perception. In this letter, a methodology to generate datasets from a mixture of synthetic and real samples for the training of DLOs segmentation approaches is thus presented. The method is composed of two steps. First, key-points along a real-world DLO are labeled by employing a VR tracker operated by a user. Second, synthetic and real-world datasets are mixed for the training of semantic and instance segmentation deep learning algorithms to study the benefit of real-world data in DLOs segmentation. To validate this method a user study and a parameter study are conducted. The results show that the VR tracker labeling is usable as other labeling techniques but reduces the number of clicks. Moreover, mixing real-world and synthetic DLOs data can improve the IoU score of a semantic segmentation algorithm by circa 5%. Therefore, this work demonstrates that labeling real-world data via a VR tracker can be done quickly and, if the real-world data are mixed with synthetic data, the performances of segmentation algorithms for DLOs can be improved.

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Section: A Segmentation Of Dlosmentioning

confidence: 99%

Section: Training On a Dataset Mixturementioning

confidence: 99%

Section: B Mixing Synthetic and Real Samples For Trainingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Weakly Supervised Semi-Automatic Image Labeling Approach for Deformable Linear Objects

Caporali

Pantano

Janisch

et al. 2023

IEEE Robot. Autom. Lett.

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Shape Control of Elastic Deformable Linear Objects for Robotic Cable Assembly

Cao,

Zang,

Zhang

et al. 2024

Advanced Intelligent Systems

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Currently, cables are manually installed in aircraft manufacturing scenarios. The utilization of robots in cable assemblies can enhance efficiency and ensure quality, presenting great promise for the future. However, the assembly process must control the shape of the cables, which is a significant challenge for robots. On the one hand, cables have high degrees of freedom in space, making accurate modeling of cable dynamics for robotic arm manipulation difficult; on the other hand, cable deformation has uncertainty, and the applied forces cause simultaneous deformation and motion, which is difficult to control. To address these problems, this study proposes a cable‐shape control method based on graph neural networks and online visual shape‐servoing. The method first approximates cable dynamics with a graph neural network. Then, in practice, the learnt model is used alongside visual‐based shape‐servoing to generate optimal robotic arm movements, controlling the cable to attain the desired shape. In the experiments, precise control of three different cable types is realized, and an example of a completed cable assembly is shown.

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Generation of realistic synthetic cable images to train deep learning segmentation models

MalvidoFresnillo,

Mohammed,

Vasudevan

et al. 2024

Machine Vision and Applications

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Semantic segmentation is one of the most important and studied problems in machine vision, which has been solved with high accuracy by many deep learning models. However, all these models present a significant drawback, they require large and diverse datasets to be trained. Gathering and annotating all these images manually would be extremely time-consuming, hence, numerous researchers have proposed approaches to facilitate or automate the process. Nevertheless, when the objects to be segmented are deformable, such as cables, the automation of this process becomes more challenging, as the dataset needs to represent their high diversity of shapes while keeping a high level of realism, and none of the existing solutions have been able to address it effectively. Therefore, this paper proposes a novel methodology to automatically generate highly realistic synthetic datasets of cables for training deep learning models in image segmentation tasks. This methodology utilizes Blender to create photo-realistic cable scenes and a Python pipeline to introduce random variations and natural deformations. To prove its performance, a dataset composed of 25000 synthetic cable images and their corresponding masks was generated and used to train six popular deep learning segmentation models. These models were then utilized to segment real cable images achieving outstanding results (over 70% IoU and 80% Dice coefficient for all the models). Both the methodology and the generated dataset are publicly available in the project’s repository.

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FASTDLO: Fast Deformable Linear Objects Instance Segmentation

Cited by 27 publications

References 27 publications

A Weakly Supervised Semi-Automatic Image Labeling Approach for Deformable Linear Objects

A Weakly Supervised Semi-Automatic Image Labeling Approach for Deformable Linear Objects

Shape Control of Elastic Deformable Linear Objects for Robotic Cable Assembly

Generation of realistic synthetic cable images to train deep learning segmentation models

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