AI Chip Recognition Learning for Piled Gaming Table Chips
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Solution Overview
Problem
Existing systems struggle to accurately recognize and learn the patterns of chips bet by players in games like baccarat, particularly when chips are piled up, leading to errors in determination.
Innovation Solution
A chip recognizing and learning system utilizing an artificial intelligence device for image analysis, combined with a teaching device that inputs correction data for learning, and a control device for error detection, enhances accuracy by learning from errors and correct determinations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If an artificial intelligence device is used to analyze chip images, then the speed of chip recognition is improved, but the accuracy of chip determination deteriorates when chips are piled up
Solution Approach 1:
The system incorporates a feedback mechanism where the teaching device provides correction information to the artificial intelligence device when determination errors are detected. This allows the AI to learn from its mistakes and improve its accuracy in recognizing piled-up chips over time, resolving the contradiction between fast recognition and accurate determination.
Solution Approach 2:
The artificial intelligence device performs self-learning by receiving and processing teaching data from the teaching device. This self-service capability enables the system to automatically improve its chip recognition accuracy without requiring manual retraining, maintaining both speed and improving precision for complex chip arrangements.
2Productivity
If the artificial intelligence device determines chip numbers and kinds, then the productivity of chip recognition is improved, but the reliability of determination deteriorates due to errors
Solution Approach 1:
The teaching device provides feedback by inputting correction data when determination errors are detected, allowing the artificial intelligence device to learn from mistakes and improve the reliability of its chip recognition while maintaining high productivity through automated processing.
Solution Approach 2:
The system performs preliminary learning actions by continuously training the artificial intelligence device with teaching data before actual chip recognition tasks. This preliminary training ensures high reliability in determination while maintaining rapid processing speeds during gameplay.
3Measurement precision
If teaching data is input to improve accuracy for error cases, then the measurement precision for difficult chip patterns is improved, but the device complexity increases
Solution Approach 1:
The artificial intelligence device performs self-learning by automatically processing teaching data provided by the teaching device. This self-service mechanism improves precision for difficult chip patterns without requiring complex external intervention or manual retraining procedures, keeping the overall system complexity manageable.
Solution Approach 2:
The teaching device serves multiple functions: it detects determination errors, provides correction data, and enables the artificial intelligence device to learn from mistakes. This multi-functionality improves precision for error cases while avoiding the need for separate complex subsystems for each function.
Data Source
AI summary
A chip recognizing and learning system includes: a game recording device configured to record a state of chips piled up on a gaming table as an image by a camera; a chip determining device including an artificial intelligence device configured to analyze the recorded image of the state of the chips to determine the numbers and kinds of chips bet by a player; and a teaching device configured to input, in a case where it is determined that there is a doubt for an error in a determining result of the chip determining device, the image used for determination of the chip determining device and the correct numbers and correct kinds of chips for the error as teaching data to the artificial intelligence device to allow the artificial intelligence device to perform learning.