Machine Parts Segmentation Dataset
Project Overview:
Objective
Our goal was to curate a dedicated dataset for segmenting machine parts in images, a critical step in advancing computer vision capabilities in manufacturing, quality control, and maintenance.
Scope
We successfully established an extensive repository of images featuring a variety of machine parts. Each image in this collection has been meticulously annotated to highlight individual components and structures.
Sources
- Collaborations with Manufacturing Facilities: We partnered with various industrial manufacturing plants to gather images of machines and components in diverse manufacturing environments.
- Technical Manual Extractions: We sourced detailed illustrations of machine parts from various technical manuals.
- Public Contributions: We set up a platform inviting individuals to submit images from their personal devices, thus enriching our dataset with a wide range of machine types and conditions.
Data Collection Metrics
- Total Images for Pest Detection: 15,000 images
- Field Surveys: 8,000
- Research Institutions: 5,000
- Agricultural Databases: 2,000
Annotation Process
Stages
- Part Segmentation: Defining boundaries within each image to segment individual machine parts.
- Machine Type Tagging: Assigning labels to each image based on the machine type, such as lathes, CNC mills, and conveyors.
- Maintenance Condition: Categorizing images by the condition of the machines or parts, like new, worn, or damaged.
Annotation Metrics
- Images with Part Segmentations: 200,000
- Machine Type Tags: 200,000
- Maintenance Condition Labels: 180,000
Quality Assurance
Stages
Segmentation Accuracy Checks: Utilizing both computer vision algorithms and human review for validating part segmentation accuracy.
Metadata Validation: Expert review from professionals in machine maintenance and industrial engineering to confirm machine type and condition tags.
Privacy Protections: We took special care to ensure public submissions did not include sensitive information. A process was established for contributors to request image alterations or removals.
QA Metrics
- Segmentation Validation Cases: 20,000
- Metadata Authentication Reviews: 30,000
- Privacy Audits: 40,000
Conclusion
Our Machine Parts Segmentation Dataset Initiative has significantly contributed to the field of computer vision in the industrial sector. Through our diverse and accurately annotated image repository, we provide a valuable resource for enhancing machine diagnostics, quality control, and predictive maintenance strategies.
Quality Data Creation
Guaranteed TAT
ISO 9001:2015, ISO/IEC 27001:2013 Certified
HIPAA Compliance
GDPR Compliance
Compliance and Security
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