Home/Industry/Wheel Hub Detection Dataset

Wheel Hub Detection Dataset

V1.0
Latest Update:
2026-01-14
Samples:
5000 records
File Size:
1.5G
Format:
JPG/PNG/JSON
Data Domain:
Image
Holder:
MOBIUSI INCMOBIUSI INC
Industry Scope:
Hub Defect Identification | Tire Assembly Pre-Inspection | Wheel Error-Proofing System
Applications:
Defect Detection | Quality Control | Automated Inspection

Brief Introduction

The Wheel Hub Detection Dataset aims to address the increasing demand for automated quality control in the automotive industry, where the accurate identification of defects is crucial for safety and performance. Current solutions often struggle with high false-positive rates and lack sufficient data diversity to train robust models. This dataset is designed to solve the problem of effective defect detection by providing a large number of labeled images under various conditions. The data is collected using high-resolution cameras in controlled lighting environments, ensuring high quality. Quality control measures include multi-round annotations, consistency checks among annotators, and expert reviews to ensure accuracy. The dataset is stored in JPEG format, organized by categories of defects and timestamps for easy access.

Sample Examples

ImageFile NameResolutionDefect TypeDefect LocationDefect SeverityRim TypeSurface Texture
fa78347a8ad35ac86d835f496820c83f.jpg1280*979StainArea below the rimLevel 2Aluminum alloy wheel rimSmooth
5c2b669bb7de45a8d824f6abbaddc8a4.jpg1280*1066No noticeable defectsNoneNo defectsMulti-spoke wheel rimSmooth
f0faca6f4546471370d36f2bb55f640c.jpg1280*1191no obvious defectsnoneno defectsstandard aluminum wheelssmooth
b0df0d0923021b31d2f044a931a2f3e4.jpg1280*1191dentouter rim edgegrade 3multi-spoke wheelssmooth metal surface, with slight scratches and stains
51481a425fb163935c8e92ced749481f.jpg1280*904no obvious defectsnoneno defectsmulti-spoke wheelssmooth

Data Structure

FieldTypeDescription
file_namestringFile name
qualitystringResolution
defect_typestringTypes of rim defects identified in the image, such as cracks, dents, etc.
defect_locationstringDescription of the specific defect location on the rim, such as a particular area or coordinates.
defect_severityintegerIndicates the severity of the defect, usually represented in levels such as 1-5.
wheel_typestringThe type or model of the rim, used to identify different kinds or specifications of rims.
surface_texturestringDescription of visible texture features on the surface of the rim.

Compliance Statement

ItemContent
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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