Capacitor and Resistor Detection Dataset

#Object Detection #Image Classification #Visual Inspection #Automated Assembly #Quality Control
  • 50000 records
  • 3.2G
  • JPG/PNG/JSON
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-03-10

AI Analysis & Value Prop

The Capacitor and Resistor Detection Dataset aims to address the increasing need for high-precision visual inspection in the industrial assembly sector. Current challenges include the high error rates in manual inspections and the lack of comprehensive datasets tailored for electronic component detection. Existing solutions often struggle with accuracy and consistency, leading to defects in the final assembly. This dataset is designed to enhance the performance of automated visual inspection systems by providing a diverse range of images of capacitors and resistors under varying conditions. The data was collected using high-resolution cameras in controlled factory environments, ensuring clarity and detail. Quality control measures included multiple rounds of annotation, consistency checks among different annotators, and expert reviews to ensure high accuracy. The dataset is organized in JPG format, with images stored in labeled folders corresponding to each component type.

Dataset Insights

Sample Examples

fa711ab9**.png|2942*1500|5.88 MB

23a600a5**.png|2669*1500|3.35 MB

c7c71db7**.png|2278*1500|3.62 MB

519fdb54**.png|1113*1500|2.11 MB

c3447b60**.png|1025*1500|1.40 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/c00193383395777026a5bb2494927b8e},
  urldate={},
  keywords={},
  version={}
}

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