Marked Product Code Inspection for Real-Time Quality Screening
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Solution Overview
Problem
Existing manufacturing processes lack real-time inspection of mark quality on products, leading to unsatisfactory mark quality and defectiveness in mass-produced items, particularly in scenarios like prismatic lithium batteries.
Innovation Solution
A method and apparatus for real-time mark quality inspection involving image acquisition and processing of marked products, including identification of identification codes, size, position, and digital codes, with indication signals for normal or abnormal results, and automatic device shutdown when consecutive issues occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional machine vision methods are used for code recognition, then the system is simple to implement, but the recognition accuracy is low due to insufficient adaptability to various code types and complex backgrounds
Solution Approach 1:
The system segments the code detection task into multiple specialized modules: a code type identification module that first determines the code format, and then routes to specific recognition modules for different code types (barcodes, QR codes, data matrices, etc.). This segmentation allows each module to specialize in specific code types, improving overall recognition accuracy while maintaining manageable system complexity through modular design.
Solution Approach 2:
The system implements dynamic adaptability by automatically adjusting detection parameters and algorithms based on the identified code type and background conditions. The code type identification module dynamically selects the appropriate recognition strategy, enabling the system to adapt to various code formats and complex backgrounds rather than using a fixed detection approach.
2Productivity
If manual detection is used for code quality, then the detection can be flexible, but the productivity is low and labor costs are high
Solution Approach 1:
The automated detection system performs self-adjustment by automatically identifying code types and selecting appropriate recognition algorithms without human intervention. The system serves itself by dynamically configuring detection parameters based on the input code characteristics, eliminating the need for manual detection while maintaining high efficiency and reducing labor costs.
Solution Approach 2:
The system achieves multi-functionality by incorporating a universal code type identification module that handles multiple code formats (barcodes, QR codes, data matrices, postal codes, etc.) through a single integrated platform. This universal approach enables high-productivity automated detection across diverse code types without requiring separate systems for each code format.
3Adaptability or versatility
If fixed detection parameters are used, then the device operation is simple, but the adaptability to different code types and backgrounds is poor
Solution Approach 1:
The system performs preliminary action by first identifying the code type before executing the recognition process. The code type identification module pre-determines the appropriate detection parameters and algorithms based on the code format, allowing the subsequent recognition module to operate with optimized settings automatically configured in advance, thus achieving high adaptability without requiring manual parameter adjustment.
Solution Approach 2:
The system implements feedback mechanisms where the code type identification results are used to dynamically adjust the detection parameters for the recognition module. This feedback loop ensures that the detection parameters are automatically optimized based on the specific code type and background conditions, achieving high adaptability while maintaining simple operation through automatic parameter adjustment.
Data Source
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AI summary
This application relates to a mark quality inspection method and apparatus, a computer device, and a storage medium. The method includes: acquiring a marked-product image obtained by photographing a marked product upon completion of a marking operation on a product; performing image identification on the marked-product image to obtain a mark quality identification result of an identification code in the marked-product image; and outputting a first indication signal under a condition that the mark quality identification result is normal, where the first indication signal is used to indicate that the marked product flows to a process subsequent to the marking operation. In embodiments of this application, mark quality problems can be found in a timely manner, reducing cases of undesirable mark quality and defectiveness of mass-produced products.