Scanner Window Dirt Detection via Speckle Counting
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Solution Overview
Problem
Imaging bar code scanners face degraded performance due to dirt and contaminants on optical windows, which obscure images and reduce scanning accuracy, especially when dirt is back-lit or front-lit, and existing solutions do not effectively detect cleanliness or alert operators about the need for cleaning.
Innovation Solution
An optical scanner system that automatically detects dirt on windows using a method involving image sampling and speckle counting, with optional pixel comparison thresholds and multi-resolution down-sampling, to determine cleanliness and alert operators when the scanner needs cleaning, regardless of illumination configuration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the scanner operates in a dirty environment, then productivity is maintained, but image quality degrades due to dirt on optical windows
Solution Approach 1:
The system captures images through the optical window and analyzes them for dirt patterns using speckle counting algorithms. When dirt is detected, the system provides feedback to alert operators to clean the window, enabling continuous operation while maintaining quality standards.
Solution Approach 2:
The scanner performs self-diagnosis by automatically analyzing captured images to detect dirt on its own optical windows, eliminating the need for external monitoring systems and enabling autonomous quality maintenance.
2Device complexity
If manual inspection methods are used to detect dirty windows, then device complexity is low, but measurement precision is insufficient to reliably detect dirt
Solution Approach 1:
The system replaces manual visual inspection with automated image analysis using speckle counting algorithms. The processor analyzes captured images to detect dirt patterns, providing objective and precise measurement without requiring complex additional hardware.
3Device complexity
If the scanner uses existing error logging methods, then device complexity remains low, but the ability to detect and alert about dirty windows is insufficient
Solution Approach 1:
The system implements a feedback loop that captures images, analyzes them for dirt using speckle counting, and triggers alerts when dirt thresholds are exceeded. This creates an automated detection and notification system that actively monitors window cleanliness.
Solution Approach 2:
The system introduces image analysis algorithms as an intermediary between the optical window and the detection process. By analyzing the captured images for speckle patterns, the system indirectly detects dirt on the window without requiring direct contact or additional sensors.
4Reliability
If the scanner alerts operators frequently, then reliability of cleaning notification is high, but loss of time due to alert management increases
Solution Approach 1:
The system provides automated feedback to operators through alerts and notifications when dirt is detected, eliminating the need for manual monitoring and reducing the time operators spend checking window cleanliness while maintaining high detection reliability.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively measures dirt on optical windows, enhancing scanner performance by alerting operators when cleaning is necessary, thereby maintaining optimal scanning quality and productivity.
Implementation Method 1
dirt, etc., from the environment, some of which may be deposited on one or more of the scanner's optical components... it may obscure the image in the places where the dirt is interposed between the object and the sensor
Data Source
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AI summary
An image from a window (130 or 401) of a scanner (110 or 402) is taken and analyzed to determine whether the image is relevant to dirt or debris on the window (130 or 401). When the image is relevant to dirt or debris, the size and amount of the dirt or debris is compared to a threshold and when the threshold is exceeded an alert is raised to have the window (130 or 401) cleaned of the dirt or debris.