Key Image Analysis for Accurate Remote Key Duplication
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Solution Overview
Problem
Existing key duplication methods, particularly those using deep learning algorithms, suffer from inaccuracies and require significant time for processing, leading to unreliable and delayed delivery of duplicate keys.
Innovation Solution
Utilizing a deep convolutional neural network algorithm to analyze key images, correcting errors in real-time, and employing background removal techniques, orientation correction, and blur reduction to determine key type, model, and cutting parameters, enabling precise and automated key duplication.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional mechanical tools are used for key duplication, then the duplication process is reliable and accurate, but the process requires physical presence at a locksmith and takes significant time
Solution Approach 1:
The patent replaces traditional mechanical key duplication tools with an automated system comprising a camera for image capture, a processing unit for image analysis and key code extraction, and an automated cutting machine. This substitution eliminates the need for manual mechanical operations while maintaining duplication accuracy and enabling remote operation, thus resolving the contradiction between reliability and time loss.
2Productivity
If automated key duplication systems are implemented, then processing time is reduced, but accuracy and reliability of key duplication deteriorate
Solution Approach 1:
The patent implements feedback mechanisms through multiple image capture angles, image quality validation, and iterative processing steps. The system captures images from different angles, validates image quality against predefined criteria, and performs multiple processing iterations to ensure accurate key code extraction. This feedback loop maintains high duplication accuracy while enabling automated rapid processing.
Solution Approach 2:
The patent performs preliminary actions by capturing multiple images before duplication, validating image quality in advance, and pre-processing images to enhance features. This preliminary preparation ensures that the automated system has sufficient quality data to work with, thereby maintaining accuracy while enabling fast subsequent processing and duplication.
3Speed
If deep learning algorithms are used for key analysis, then processing speed improves, but errors in key identification occur
Solution Approach 1:
The patent corrects errors made by the deep learning algorithm as they occur through feedback mechanisms. The system validates algorithm outputs against multiple image angles and predefined key characteristics, and performs iterative refinement of key code extraction. This feedback process maintains high processing speed while correcting identification errors.
Solution Approach 2:
The patent employs partial or excessive action by capturing more images than the minimum single image, using multiple angles and views. This excess data provides redundant information that helps the deep learning algorithm overcome individual image limitations and errors, thereby maintaining high speed processing while improving identification reliability through data redundancy.
4Ease of operation
If manual quote preparation is used, then customer approval can be obtained, but the overall process duration is lengthened
Solution Approach 1:
The patent implements self-service through automated quote generation and customer notification systems. The processing unit automatically generates duplication quotes based on analyzed key images and sends them to customers via electronic communication. Customers can approve or reject quotes remotely without manual locksmith intervention, maintaining ease of operation while dramatically reducing the time required for quote preparation and approval.
Data Source
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AI summary
The invention relates to a method for duplicating a key for a door leaf from an image of the key, comprising the steps of determining: a) the key type, b) the key blank, c) the key cut, wherein steps a) and b) are carried out by means of at least one deep learning algorithm.