3D Barcode Reader Fusion for Higher Decode and Object Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing symbology readers, such as handheld barcode readers and bi-optic readers, face limitations in detection success rates and functionality due to the use of two-dimensional image data, which restricts image analysis and fails to leverage three-dimensional data for enhanced performance and additional functionalities.
Innovation Solution
Integrating a two-dimensional (2D) imaging apparatus with a three-dimensional (3D) imaging apparatus in barcode readers to capture and process both 2D and 3D images, allowing for the correlation of features between the two to enhance image data, enabling improved barcode decoding, object recognition, and detection of actions or events.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If 2D imaging apparatus is used for barcode scanning, then the device structure is simple, but the detection success rate and functionality are limited
Solution Approach 1:
The patent combines a 2D imaging apparatus and a 3D imaging apparatus into a single barcode reader device. The 2D imager captures 2D images for traditional barcode decoding, while the 3D imager captures 3D images for additional functionalities such as object recognition, theft detection, and operator action analysis. This merging allows the system to achieve higher detection success rates and enhanced functionality without requiring separate devices.
Solution Approach 2:
The barcode reader is designed with multi-functionality by integrating both 2D and 3D imaging capabilities. The system can perform traditional barcode decoding using 2D images, object recognition using 3D images, theft detection by analyzing operator actions, and determination of product nature. This universal design allows a single device to serve multiple purposes, improving detection success rates across various scanning scenarios.
2Adaptability or versatility
If 2D image data is used for analysis, then the processing is straightforward, but the image analysis capability is limited
Solution Approach 1:
The patent transitions from 2D image analysis to 3D image analysis by incorporating a 3D imaging apparatus. The 3D imager captures depth information and spatial relationships, enabling advanced image analysis capabilities such as object recognition, determination of product nature, and detection of theft attempts. This dimensional enhancement provides richer data for analysis while maintaining manageable processing complexity through integrated processing circuits.
3Measurement precision
If bi-optic reader uses 2D image data for off-platter weighing condition detection, then the functionality is extended, but the detection accuracy is insufficient
Solution Approach 1:
The patent introduces 3D image data as an intermediary to enhance the detection of off-platter weighing conditions. The 3D imager captures spatial information that helps determine whether a product is properly positioned on the weighing platter. By correlating 3D image features with 2D image features, the system achieves higher detection accuracy for weighing conditions while managing processing complexity through integrated circuits.
4Measurement precision
If 2D image data is used for product nature determination, then the processing is simple, but the accuracy of product identification is limited
Solution Approach 1:
The patent uses 3D image data to enhance product identification accuracy by adding depth and spatial information to the analysis. The 3D imager captures the physical characteristics and geometry of products, enabling more accurate determination of product nature. This dimensional enhancement allows the system to distinguish between similar products more effectively while managing imaging system complexity through integrated processing.
Data Source
AI summary
At least some embodiments described herein are directed to a machine vision method, which involves capturing both a two-dimensional (2D) image of an object to identify a barcode and determine 3D features, and a three-dimensional (3D) image of the environment. The 3D image data is examined for the presence of the 3D object features. If a feature is missing, a digital fault detection signal is provided. If a feature is present, at least one parameter of the machine vision system is adjusted. The method also includes decoding the barcode and determining the object's orientation based on the barcode location in the 2D image.