Dart Pin Hit Location Identification Using Deep Learning
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Solution Overview
Problem
Existing image analysis methods for identifying hit locations of dart pins in dart games face challenges such as noise in various installation environments, difficulty in securing a large enough dataset, and complexity in processing hit locations of multiple dart pins.
Innovation Solution
A method and system for generating a training dataset to improve the accuracy of identifying hit locations of dart pins, utilizing a dart game apparatus equipped with cameras to capture images from multiple angles, and employing a deep learning model to process these images and generate hit location information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If rule-based algorithms are used for image analysis, then the system is simple to implement, but accuracy deteriorates due to noise in various installation environments
Solution Approach 1:
The patent replaces rule-based algorithms with a deep learning-based image analysis system. The deep learning model (neural network) automatically learns optimal feature extraction and hit location determination from training data, substituting the manual rule-based approach with an adaptive intelligent system that maintains high accuracy across varying installation environments and lighting conditions.
2Measurement precision
If deep learning is used for image analysis, then accuracy improves, but the difficulty of securing a large enough dataset increases
Solution Approach 1:
The patent employs data augmentation techniques to create synthetic training data by copying and transforming existing images. The system generates additional training samples through geometric transformations (rotation, flipping, scaling), color adjustments, and noise addition, effectively multiplying the available training data without requiring proportional increases in physical experimentation or data collection.
3Measurement precision
If deep learning is used to process hit locations of multiple dart pins, then identification accuracy improves, but algorithm complexity increases
Solution Approach 1:
The patent implements a segmentation approach where the deep learning model first identifies and segments individual dart pins in the image, then processes each segmented region separately to determine hit locations. This divides the complex task of analyzing multiple overlapping pins into simpler sub-tasks, making the algorithm more manageable while maintaining accuracy in identifying multiple hit locations simultaneously.
Data Source
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AI summary
Disclosed is a computer program stored in a computer-readable storage medium, the computer program providing a dart pin hit location identification method when being executed by one or more control units of a dart game device, the method including: obtaining at least two dart target photographing images in response to a dart pin throw, in which the dart target photographing image includes an image of a dart target and at least one dart pin hitting the dart target; and generating, by a dart pin location identification network model, hit location information of the at least one dart pin based on the at least two dart target photographing images.