Key Image Analysis for Reliable Automated Key Duplication
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Solution Overview
Problem
Existing key duplication methods from images are unreliable and time-consuming, particularly when using automated systems, due to inaccuracies in determining key type and blank selection, and require manual quote approval, prolonging the overall process.
Innovation Solution
Utilizing deep learning algorithms, specifically deep convolutional neural networks, to analyze key images for type and blank determination, correcting errors through operator feedback, and automating the cutting process for precise and fast key duplication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If conventional automated key duplication systems are used, then the process can be automated, but the reliability and accuracy of copying original keys is insufficient
Solution Approach 1:
The patent replaces conventional image processing algorithms with deep learning neural networks to analyze key images and determine key types and blanks. This substitution of mechanical/image processing methods with intelligent algorithms significantly improves the reliability and accuracy of automated key duplication while maintaining full automation.
Solution Approach 2:
The system implements feedback mechanisms where the deep learning model is continuously trained and improved using actual key duplication results and customer corrections. This feedback loop enhances the reliability of the automated system over time, allowing it to learn from errors and improve accuracy without reducing automation level.
2Measurement precision
If conventional image processing methods are used, then the system is simpler, but the accuracy in determining key type and blank is insufficient
Solution Approach 1:
The patent replaces simple image processing algorithms with deep learning neural networks, accepting increased system complexity in exchange for dramatically improved measurement precision in key type and blank determination. The neural networks can accurately identify key characteristics that conventional methods miss.
Solution Approach 2:
The system performs preliminary actions by pre-training deep learning models on extensive key databases before actual duplication. This preliminary training phase, while complex, enables high accuracy during actual operation without requiring complex real-time processing.
3Loss of time
If manual quote approval is required, then pricing can be accurate, but the overall process time is lengthened
Solution Approach 1:
The system implements self-service by enabling automated quote generation and customer approval through digital interfaces. Customers can review and approve quotes electronically without manual locksmith intervention, dramatically reducing process time while maintaining pricing accuracy through automated calculations based on key analysis.
Solution Approach 2:
The deep learning system performs preliminary analysis of the key image to determine key type, blank, and associated costs before customer ordering. This preliminary action enables immediate quote generation, eliminating waiting time for manual assessment while ensuring accurate pricing through consistent algorithmic evaluation.
Data Source
AI summary
The invention relates to a method for duplicating a key for an opening from an image of this key, comprising the steps of determining: a) the type of key, b) the blank of the key, c) the cutting of the key, wherein steps a) and b) are performed by means of at least one deep learning algorithm.