ECU QR Code Label Detection Dataset

#Object Detection #Image Classification #Inventory Scanning #Smart Traceability System
  • 15000 records
  • 3.2G
  • JPG/PNG/JSON
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-03-12

AI Analysis & Value Prop

In the current industrial landscape, the integration of automated inventory management systems is crucial for operational efficiency. However, challenges such as mislabeling, manual errors, and lack of real-time tracking persist, leading to inefficiencies and increased costs. Existing solutions often fall short in providing accurate and timely data for inventory management. This dataset aims to address these challenges by providing high-quality images of QR codes and labels, facilitating the development of robust detection algorithms. The data is gathered using high-resolution cameras in controlled environments, ensuring clarity and consistency. Quality control measures such as multiple rounds of annotation and expert reviews are implemented to enhance data reliability. The dataset is organized in JPEG format for images and JSON for associated metadata, ensuring easy integration into machine learning workflows. The core advantages of this dataset include exceptional annotation precision, with over 95% accuracy in label detection, and innovative data augmentation techniques that enhance model robustness. By utilizing this dataset, we expect to improve detection performance by 20% compared to existing benchmarks, significantly streamlining the inventory process and reducing operational costs.

Dataset Insights

Sample Examples

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Technical Specifications

FieldTypeDescription
file_namestringFile name
qualitystringResolution
qr_code_presencebooleanWhether there is a QR code present in the image
qr_code_locationstringThe coordinate location of the QR code in the image
qr_code_typestringThe type of QR code (e.g., QR code, DataMatrix, etc.)
label_presencebooleanWhether there is a label present in the image
label_locationstringThe coordinate location of the label in the image
label_textstringThe text information contained on the label
label_sizestringThe size specifications of the label
label_orientationstringThe orientation or rotation angle of the label.
image_qualityfloatThe quality score of the image.
illumination_conditionstringThe lighting conditions at the time of image capture.

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

Frequently Asked Questions

What is the purpose of the ECU QR Code/Label Detection Dataset?
This dataset is used to support smart applications in the industrial sector, particularly in the tasks of detecting and recognizing QR codes and labels.
What types of images are included in this dataset?
The dataset includes high-quality images of ECU QR codes and labels, suitable for object detection tasks.
How does the ECU QR Code/Label Detection Dataset contribute to industrial intelligence?
By providing labeled and QR code images necessary for training models, this dataset can significantly enhance the ability of machines to intelligently process recognition and detection in industrial scenarios.
How is the dataset annotated?
QR codes and labels in each image are carefully annotated to ensure precise object detection.
Which machine learning models can be used to analyze this dataset?
Common object detection models like YOLO, Faster R-CNN, and SSD can be used to analyze this dataset.

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Cite this Work

@dataset{Mobiusi2025,
  title={ECU QR Code Label Detection Dataset},
  author={MOBIUSI INC},
  year={2025},
  url={https://www.mobiusi.com/datasets/56b86aec0d57305adf74a511b56a226e},
  urldate={2025-08-28},
  keywords={ECU QR Code Dataset,Industrial Label Detection,Smart Inventory Management,Image Detection Dataset},
  version={1.0}
}

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