Hyundai motor company (20240104901). APPARATUS FOR SELECTING A TRAINING IMAGE OF A DEEP LEARNING MODEL AND A METHOD THEREOF simplified abstract
Contents
- 1 APPARATUS FOR SELECTING A TRAINING IMAGE OF A DEEP LEARNING MODEL AND A METHOD THEREOF
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 APPARATUS FOR SELECTING A TRAINING IMAGE OF A DEEP LEARNING MODEL AND A METHOD THEREOF - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
APPARATUS FOR SELECTING A TRAINING IMAGE OF A DEEP LEARNING MODEL AND A METHOD THEREOF
Organization Name
Inventor(s)
Jin Sol Kim of Hwaseong-si (KR)
APPARATUS FOR SELECTING A TRAINING IMAGE OF A DEEP LEARNING MODEL AND A METHOD THEREOF - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240104901 titled 'APPARATUS FOR SELECTING A TRAINING IMAGE OF A DEEP LEARNING MODEL AND A METHOD THEREOF
Simplified Explanation
The patent application describes an apparatus and method for selecting a training image for a deep learning model based on the similarity between objects in a simulation image and a training image.
- The input device receives a simulation image and object information from a simulation tool, as well as a training image from an image conversion device.
- The controller detects the similarity between the structure of objects in the simulation image and the training image to determine the validity of the training image.
Potential Applications
This technology can be applied in various fields such as computer vision, autonomous driving, robotics, and medical imaging.
Problems Solved
1. Efficient selection of training images for deep learning models. 2. Improved accuracy and performance of deep learning models by using relevant training images.
Benefits
1. Enhanced training process for deep learning models. 2. Increased accuracy and efficiency in object recognition tasks. 3. Reduction in manual effort required for selecting training images.
Potential Commercial Applications
Optimizing training image selection in industries such as healthcare, manufacturing, security, and agriculture can lead to improved product quality, cost savings, and increased productivity.
Possible Prior Art
Prior art may include methods for image recognition and object detection in deep learning models, as well as techniques for image preprocessing and data augmentation.
Unanswered Questions
How does the apparatus handle variations in object appearance between the simulation image and training image?
The apparatus may use feature extraction techniques or image registration algorithms to account for differences in object appearance.
What is the computational overhead of the apparatus in determining the validity of training images?
The computational overhead may vary depending on the complexity of the objects in the images and the size of the training dataset.
Original Abstract Submitted
an apparatus for selecting a training image of a deep learning model and a method thereof are disclosed. the apparatus includes an input device and a controller. the input device receives a simulation image and information about an object in the simulation image from a simulation tool and receives a training image corresponding to the simulation image from an image conversion device. the controller detects a similarity between a structure of the object in the simulation image and a structure of an object in the training image and determines validity of the training image based on the detected similarity.
