Comparative Statistics for Vision, CMM, and CNN Measurement Techniques

A 5.5 KB dataset provides descriptive statistics comparing measurement techniques for industrial workpiece dimension measurement. The dataset, created by Yazid Saif and last updated in March 2026, focuses on techniques including Vision Inspection (VI), Coordinate Measuring Machines (CMM), and Convolutional Neural Networks (CNN). It addresses topics such as edge detection, varying hole sizes, and interference regions in machine vision applications.

Use Cases

• Compare the precision of Vision Inspection (VI), Coordinate Measuring Machines (CMM), and Convolutional Neural Network (CNN) techniques for workpiece dimension measurement.
• Analyze the impact of varying hole sizes and complicating edge detection on measurement accuracy across different techniques.
• Benchmark the performance of a CNN with fully connected layers against traditional vision systems in identifying interference regions.

Strengths

• Dataset is licensed under CC BY 4.0, permitting open sharing and adaptation.
• Focuses on a specific industrial application: workpiece dimension measurement using multiple techniques.
• Tags indicate coverage of concrete metrics, such as 'Cnn Achieved 100' and 'Matching Cmm Precision'.

Limitations

• Extremely small file size (5.5 KB) suggests a very limited scope, likely containing only summary statistics.
• No column names, sample data, or row count is provided, limiting understanding of the data structure.
• The description is minimal, offering no details on the experimental setup, sample size, or data collection methodology.

Provenance

Source

figshare

Collection Method

Descriptive statistics from a comparative study of measurement techniques, methodology unknown.

Freshness

Last updated March 2026.