Oil Sump Detection Dataset
#Object Detection #Image Classification #Industrial Inspection #Quality Control #Automotive Engineering
- 5000 records
- 1.8G
- JPG/PNG/JSON
- CC-BY-NC-SA 4.0
MOBIUSI INC
Updated:2026-02-04
AI Analysis & Value Prop
The Oil Sump Detection Dataset addresses the growing need for reliable industrial inspection methods in automotive manufacturing, where traditional manual inspections are often time-consuming and error-prone. Existing solutions typically lack the accuracy required to meet quality standards, leading to defects in engine assembly. This dataset aims to enhance automated detection capabilities, contributing to improved operational efficiency. Data collection was conducted using high-resolution cameras under controlled lighting conditions to ensure consistency. Quality control measures include multiple rounds of annotation by skilled annotators, consistency checks, and expert reviews to validate the annotations. The dataset is structured with images stored in JPG format, organized by categories based on the type of defects detected.
Dataset Insights
Sample Examples
11ec5c12**.jpg|1600*1200|420.12 KB
8276413b**.jpg|1440*1920|492.24 KB
26dda0d2**.jpg|1080*1440|212.07 KB
ae48a889**.jpg|1200*900|162.10 KB
5540bc2c**.jpg|1600*1200|337.43 KB
Technical Specifications
| Field | Type | Description |
|---|---|---|
| file_name | string | File name |
| quality | string | Resolution |
| crack_presence | boolean | This field marks whether there is a crack present in the image. |
| corrosion_level | string | Indicates the level of corrosion on the surface of the oil pan (e.g., no corrosion, mild, moderate, severe). |
| hole_count | int | The number of detectable holes in the image. |
| scratch_length | float | The length of the largest scratch in the image (unit: millimeters). |
| dirt_coverage | float | The proportion of the oil pan surface covered by dirt (a floating point number between 0 and 1). |
| part_alignment | string | The alignment status of components in the image (e.g., aligned, misaligned). |
| paint_condition | string | The condition of the surface coating (intact, worn, peeling). |
| weld_quality | string | The quality status of the welding (e.g., good, average, poor). |
| oil_leakage | boolean | Whether the image shows evidence of oil leakage. |
Compliance Statement
| Authorization Type | CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike) |
| Commercial Use | Requires exclusive subscription or authorization contract (monthly or per-invocation charging) |
| Privacy and Anonymization | No PII, no real company names, simulated scenarios follow industry standards |
| Compliance System | Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs |
Frequently Asked Questions
- What industrial applications is this dataset suitable for?
- The oil pan inspection dataset is mainly used for engine system inspection and maintenance, especially for the development of automated inspection systems.
- What problems can be solved using the oil pan inspection dataset?
- This dataset can help identify and detect defects in engine oil pans, improving the accuracy and efficiency of machine vision systems.
- Why choose images as the modality for the oil pan inspection dataset?
- Images can accurately capture the details of the engine bottom, providing a high-resolution perspective for object detection.
- How does the oil pan inspection dataset perform in object detection?
- This dataset performs excellently in the task of detecting engine bottom housings due to its detailed and high-resolution images.
- What is the image quality of the oil pan inspection dataset?
- The dataset provides high-quality images suitable for industrial applications requiring fine detection.
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Verified for Enterprise UseCite this Work
@dataset{Mobiusi2025,
title={Oil Sump Detection Dataset},
author={MOBIUSI INC},
year={2025},
url={https://www.mobiusi.com/datasets/2657c3bff9c28a936231938f8b24497a?dataset_scene_id=2},
urldate={2025-08-28},
keywords={Oil Sump Detection,Industrial Dataset,Object Detection Dataset,Automotive Inspection},
version={1.0}
}Using this in research? Please cite us.
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