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- Document type:
- Forschungsdaten
- Publication date:
- 14.12.2022
- Responsible:
- Dirr, Jonas
- Authors:
- Dirr, Jonas; Yao, Jiajun; Siepmann, Andre; Gebauer, Daniel; Daub, Rüdiger
- Author affiliation:
- TUM
- Publisher:
- TUM
- Title:
- Dataset for Instance Segmentation of Deformable Linear Objects
- Identifier:
- doi:10.14459/2022mp1690303
- End date of data production:
- 12.08.2022
- Subject area:
- DAT Datenverarbeitung, Informatik; MAS Maschinenbau
- Resource type:
- Abbildungen von Objekten / image of objects
- Data type:
- Bilder / images
- Description:
- This dataset contains images of deformable linear objects (DLOs) in bin picking applications. The images show cables from control cabinet construction, which are provided in small load carriers. The dataset is divided into three scenarios, which differ in terms of cable arrangement:
>S1 Individual provision: One single DLO is provided in the small load carrier.
>S2 Semi-structured provision: Multiple DLOs are provided in the small load carrier, but they do not overlap.
>S3 Unstructured provision: In a small load carrier, multiple DLOs are provided and may overlap or cross each other.
For each scenario, one subfolder with about 80 real-world images in JPG format is provided. The manually annotated instance masks are provided in COCO format as JSON files. In addition, the instance masks of the cables are visualized as JPG images. - Links:
- This dataset relates to the publication: https://doi.org/10.3390/s23063013
- Key words:
- deformable linear object; cable; wire; instance segmentation; bin picking, object detection
- Technical remarks:
- View and download (45 MB total, 493 Files)
The data server also offers downloads with FTP
The data server also offers downloads with rsync (password m1690303):
rsync rsync://m1690303@dataserv.ub.tum.de/m1690303/ - Language:
- en
- Rights:
- by-sa, http://creativecommons.org/licenses/by-sa/4.0
BibTeX