Barcode Detection Using Color and Gray Value Features
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Solution Overview
Problem
Existing barcode detection methods fail to reliably identify objects with damaged, difficult-to-read, or partially covered barcodes, requiring high effort and resolution, leading to reduced throughput and increased costs.
Innovation Solution
A method that uses additional features from color and gray value image data, such as background color, pixel proportions, contrast, and object shape, to identify objects even with incompletely captured barcodes, without needing additional sensors, allowing for higher throughput and lower resolution requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If high resolution optical sensors are used to detect barcodes, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The barcode detection process is segmented into multiple evaluation stages: initial barcode component detection, quality assessment of detected components, and conditional determination of additional features. This segmentation allows the system to use lower resolution sensors by processing information in stages rather than requiring all details to be captured at high resolution simultaneously.
Solution Approach 2:
The system changes the parameter of resolution requirements by introducing additional feature determination (color, gray value, geometric properties) as an alternative compensation mechanism. When barcode components are incompletely detected, the system switches to evaluating additional features from the same image data, effectively changing the resolution parameter requirement from high to acceptable/low.
2Reliability
If manual handling and object separation are used for damaged barcodes, then identification reliability is improved, but productivity decreases
Solution Approach 1:
The system implements self-service by automatically detecting and evaluating additional features (color, gray value, geometric properties) when barcodes are damaged or incompletely captured. This automated self-correction eliminates the need for manual handling and object separation, maintaining both high reliability and productivity simultaneously.
Solution Approach 2:
The system uses feedback mechanisms by continuously assessing the quality of detected barcode components and automatically triggering additional feature determination when quality thresholds are not met. This closed-loop feedback process ensures reliable identification without manual intervention, maintaining high throughput by keeping objects moving through the detection system.
3Measurement precision
If additional sensors are deployed to detect damaged barcodes, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The optical sensor system is designed with multi-functionality to extract multiple types of information (barcode components, color data, gray value information, geometric properties) from the same image capture. This universal approach allows one sensor to perform multiple detection functions, eliminating the need for additional specialized sensors while maintaining detection accuracy for damaged barcodes.
Data Source
AI summary
The method involves scanning the bar code and the surroundings of the bar code by an optical sensor and receiving color- or gray scale image data of the bar code and the surroundings of the bar code. A characteristic of the bar code or the surroundings of the bar code is determined from the evaluated color and gray-scale image data.