Dataset Information

Automatic Image Generation Pipeline for Instance Segmentation of Deformable Linear Objects.

ABSTRACT: Robust detection of deformable linear objects (DLOs) is a crucial challenge for the automation of handling and assembly of cables and hoses. The lack of training data is a limiting factor for deep-learning-based detection of DLOs. In this context, we propose an automatic image generation pipeline for instance segmentation of DLOs. In this pipeline, a user can set boundary conditions to generate training data for industrial applications automatically. A comparison of different replication types of DLOs shows that modeling DLOs as rigid bodies with versatile deformations is most effective. Further, reference scenarios for the arrangement of DLOs are defined to generate scenes in a simulation automatically. This allows the pipelines to be quickly transferred to new applications. The validation of models trained with synthetic images and tested on real-world images shows the feasibility of the proposed data generation approach for segmentation of DLOs. Finally, we show that the pipeline yields results comparable to the state of the art but has advantages in reduced manual effort and transferability to new use cases.

SUBMITTER: Dirr J

PROVIDER: S-EPMC10058460 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature

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Automatic Image Generation Pipeline for Instance Segmentation of Deformable Linear Objects.

Dirr Jonas J Gebauer Daniel D Yao Jiajun J Daub Rüdiger R

Sensors (Basel, Switzerland) 20230310 6

Robust detection of deformable linear objects (DLOs) is a crucial challenge for the automation of handling and assembly of cables and hoses. The lack of training data is a limiting factor for deep-learning-based detection of DLOs. In this context, we propose an automatic image generation pipeline for instance segmentation of DLOs. In this pipeline, a user can set boundary conditions to generate training data for industrial applications automatically. A comparison of different replication types o ...[more]

PMID: 36991728

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Project description:Recently, there has been growing interest in deep spectral methods for image localization and segmentation, influenced by traditional spectral segmentation approaches. These methods reframe the image decomposition process as a graph partitioning task by extracting features using self-supervised learning and utilizing the Laplacian of the affinity matrix to obtain eigensegments. However, instance segmentation has received less attention than other tasks within the context of deep spectral methods. This paper addresses that not all channels of the feature map extracted from a self-supervised backbone contain sufficient information for instance segmentation purposes. Some channels are noisy and hinder the accuracy of the task. To overcome this issue, this paper proposes two channel reduction modules, Noise Channel Reduction (NCR) and Deviation-based Channel Reduction (DCR). The NCR retains channels with lower entropy, as they are less likely to be noisy, while DCR prunes channels with low standard deviation, as they lack sufficient information for effective instance segmentation. Furthermore, the paper demonstrates that the dot product, commonly used in deep spectral methods, is not suitable for instance segmentation due to its sensitivity to feature map values, potentially leading to incorrect instance segments. A novel similarity metric called Bray-curtis over Chebyshev (BoC) is proposed to address this issue. This metric considers the distribution of features in addition to their values, providing a more robust similarity measure for instance segmentation. Quantitative and qualitative results on the Youtube-VIS 2019 and OVIS datasets highlight the improvements achieved by the proposed channel reduction methods and using BoC instead of the conventional dot product for creating the affinity matrix. These improvements regarding mean Intersection over Union (mIoU) and extracted instance segments are observed, demonstrating enhanced instance segmentation performance. The code is available on: https://github.com/farnooshar/SpecUnIIS.

Dataset Information

Automatic Image Generation Pipeline for Instance Segmentation of Deformable Linear Objects.

Publications

Automatic Image Generation Pipeline for Instance Segmentation of Deformable Linear Objects.

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