Folding Chair Joint Slot Detection Dataset

#object detection #object recognition #product inspection #quality control #inventory management
  • 5000 records
  • 1.2G
  • JPG/PNG/JSON
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-03-05

AI Analysis & Value Prop

With the rapid development of retail e-commerce, product quality and detection efficiency have become important challenges in the industry, especially in the field of object detection. Traditional methods often fail to meet the needs for high efficiency and high precision. Existing solutions such as manual inspection are not only costly but also inefficient and unable to meet large-scale product inspection demands. This dataset aims to provide a high-quality folding chair joint slot detection dataset to support the development and optimization of automated detection systems. Data collection is conducted with professional cameras under standard lighting conditions to ensure image clarity and consistency. Quality control is carried out through multiple rounds of annotation and expert review to ensure the accuracy and comprehensiveness of the data. Data is stored in JPEG format with a clear structure, facilitating subsequent processing and analysis.

Dataset Insights

Sample Examples

15de0aeb**.png|1122*1400|2.12 MB

73ae11cd**.png|1045*1400|1.52 MB

71b10cc0**.png|1046*1400|1.99 MB

4a435911**.png|1059*1400|1.78 MB

498bb60a**.png|1298*1400|2.50 MB

Technical Specifications

Compliance Statement

Authorization TypeCC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial UseRequires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and AnonymizationNo PII, no real company names, simulated scenarios follow industry standards
Compliance SystemCompliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

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Cite this Work

@dataset{Mobiusiundefined,
  title={},
  author={Mobiusi},
  year={undefined},
  url={https://www.mobiusi.com/datasets/0f68877c67c4d7e98944a5ff0e70b1fb?dataset_scene_id=9},
  urldate={},
  keywords={},
  version={}
}

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