Tech Papers
Understanding Machine Vision Verification of 1D and 2D Barcodes
POSTED 04/12/2013 | By: Barcode Quality for Reliable Process Operation
Legible, accurate barcodes have never been more important than they are today, when automated supply chains depend on data accuracy to ensure the reliable performance of global operations. Machine vision verification is one tool that can be used to ensure that barcodes meet a consistent level of quality for readability in an automated process, and that bad codes are identified before they result in costly failures. This white paper introduces 1D and 2D barcode verification and identifies parameters for verifying codes against published standards.
- Why Verify?
- When Should You Verify?
- Validation vs. Verification
- What Kind of Solution Is Required?
- Verification Evaluation Parameters
- Verification Grades
- Verification with AutoVISIONTM
Why Verify?
Barcode quality is integral to the success of an automated system. In a process where quality barcodes accurately store and communicate data – from code to reader to central system – little manual intervention is required. Thanks to quality barcodes, the unique benefits of an automated system are realized: lower costs, higher productivity, and fewer errors. Poor quality barcodes, however, render the system almost as inefficient as using no automation at all. Unreadable barcodes may require re-labeling, re-scanning, or even manual entry of critical information by a human operator – disrupting the productivity of the process and causing a significant loss of time. Bad barcodes may prevent error-tracking, causing a domino effect of failures down the line and resulting in costly scrap and rework. All told, these effects completely counteract the benefits of implementing an automated system, the result being inflated cost, loss of productivity, and increased errors.
The purpose of barcode verification is to prevent this outcome and preserve the intended benefits of the automated system. Verification systems evaluate a barcode’s quality against published quality standards for 1D and 2D barcodes using precision instruments such as barcode verifiers or machine vision systems. A verified barcode ensures consistent readability, supporting 100% accurate automated data capture.
When Should You Verify?
To ensure that errors are prevented as early in the automated system as possible, verification must occur before a part enters the system. A verification step should occur after a part is marked or labeled with a barcode and before the part reaches the station where the barcode is first read.
Proper verification ensures that every part is processed and shipped with a high-quality barcode, despite the fact that marking and labeling systems will degrade over time. A verification system is much more accurate than a standard barcode reader at identifying low-quality barcodes early in the process, before parts with bad barcodes make it through the line and are shipped to end customers. When barcode quality degradation is identified early, the marking or labeling system may be adjusted or replaced before unreadable barcodes are ever produced.
Without verification, bad barcodes are not identified until they are unreadable. By the time a bad barcode is identified, conceivably thousands of poor-quality barcodes may have already escaped down the line.
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With verification, bad barcodes are prevented from being applied to the product, eliminating the chance for future failures.
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Validation vs. Verification
What Kind of Solution Is Required?
Selecting the right light and camera combination is key to the success of the application once an appropriate software platform has been chosen. More precise quality grading requires a higher-performance hardware solution. A smart camera with integrated optics and lighting will often be suitable for validation; however, barcodes that must comply with published barcode quality standards must be verified by a system with superior optics and with complete and uniform lighting by an ISO/AIM-compliant light to product an undistorted image.
Validation/Process Control: Fully-Integrated Smart Camera Vision System with Adaptable Software for Customized Validation |
Verification: C-Mount Smart Camera Vision System with External Lighting and Standards-Based Verification Software |
Parameter
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Description
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Example
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ISO 15416
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Decodability |
Legibility per a reference decode algorithm |
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√ |
Defects |
Voids in bars or spots in spaces |
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√ |
Edge Determination |
Detection of all bars and spaces using a global threshold |
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√ |
Minimum Edge Contrast |
Minimum reflectance difference for any bar/space combination |
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√ |
Minimum Reflectance |
Reflectance of the darkest bar and the lightest space |
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√ |
Modulation |
Relation between wide and narrow elements in the symbol |
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√ |
Symbol Contrast |
Difference in reflectance between the darkest bar and the lightest space |
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√ |
Quiet Zone |
Size of the quiet zone |
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√ |
Parameter
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Description
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Example
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ISO 15415
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AIM DPM
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Axial Non-Uniformity |
Amount of deviation along a symbol’s major axes |
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√ |
√ |
Symbol Contrast |
Difference in reflectance between light and dark symbol elements |
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√ |
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Cell Contrast |
Difference in grayscale value between light and dark symbol elements |
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|
√ |
Modulation |
Difference in reflectance of light and dark symbol elements |
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√ |
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Cell Modulation |
Deviation in grayscale values of symbol elements |
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|
√ |
Decodability |
Legibility per a reference decode algorithm |
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√ |
√ |
Fixed Pattern Damage |
Damage to the quiet zone, finder pattern, or clock pattern |
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√ |
√ |
Grid Non-Uniformity |
Amount of deviation of grid intersection |
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√ |
√ |
Minimum Reflectance |
Minimum reflectance of light elements |
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|
√ |
Reflectance Margin |
Degree to which each module is correctly distinguishable in comparison to the global threshold |
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√ |
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Unused Error Correction |
Remaining error correction available |
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√ |
√ |
Print Growth |
Variation of element size that could impede readability |
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For Reference Only
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ISO 15415 AIM DPM ISO 15416
Clear and concise values are provided via the AutoVISION user interface to grade 1D and 2D barcodes for each parameter required by a particular barcode quality standard. AutoVISION assigns values 0-4/A-F to the barcode for each parameter and then the barcode receives an overall grade for meeting the quality standard.
Default parameters in AutoVISION are pre-set to grade barcodes against published barcode quality standards (ISO 15415, ISO 15416, and AIM DPM), but can be adjusted in the AutoVISION Symbol Quality Verification Tool to enable process control grading for barcodes that must meet application-specific criteria only.
Verification evaluation parameters are adjusted in the AutoVISION Symbol Quality Verification Tool to grade a barcode for internal process control.
From 2D barcode verifiers to complete, scalable machine vision systems like AutoVISION, Microscan offers a range of products to ensure that automation systems operate at peak performance thanks to quality and compliant barcodes. For engineers tasked with meeting quality control or global standards in marking and labeling, Microscan provides project evaluations to find the right barcode verification solution for any project.