The doi above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
doi: 10.4121/611adbc7-7935-43a6-8c3f-b2260a508e73

Schoonbeek, Tim J.; Balachandran, Goutham; Onvlee, Hans; Houben, Tim; Hung, Shao-Hsuan et. al. (2024): Extension to IndustReal Dataset: Assembly State Recognition with Error States. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/611adbc7-7935-43a6-8c3f-b2260a508e73.v1

Dataset

• Artificial Intelligence and Image Processing
• Information and Computing Sciences

assembly error, assembly state recognition, computer vision, egocentric, error recognition, industrial AI, industry 4.0, procedure error, procedure understanding

Apache-2.0

RefWorks, BibTeX, Reference Manager, Endnote, DataCite, NLM, DC, CFF

by Tim J. Schoonbeek , Goutham Balachandran , Hans Onvlee , Tim Houben , Shao-Hsuan Hung , Jacek Kustra , Peter H.N. de With , Fons van der Sommen

This dataset contains the assembly state images and annotations as an extension to the public IndustReal dataset. Whereas the original dataset only classifies assembly errors as "error class", we define fine-grained annotations and error type classifications for every assembly error in the dataset. These labels enable researchers to continue studying procedural and execution errors in assembly and maintenance tasks.

• 2024-10-28 first online, published, posted

4TU.ResearchData

images/jpg, annotations/json

Supervised Representation Learning towards Generalizable Assembly State Recognition

• https://timschoonbeek.github.io/state_rec.html

• https://doi.org/10.4121/b008dd74-020d-4ea4-a8ba-7bb60769d224

Eindhoven University of Technology, Department of Electrical Engineering, and
ASML Research