Hyundai motor company (20240338949). OBJECT RECOGNITION APPARATUS FOR AN AUTONOMOUS VEHICLE AND AN OBJECT RECOGNITION METHOD THEREFOR simplified abstract
Contents
- 1 OBJECT RECOGNITION APPARATUS FOR AN AUTONOMOUS VEHICLE AND AN OBJECT RECOGNITION METHOD THEREFOR
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 OBJECT RECOGNITION APPARATUS FOR AN AUTONOMOUS VEHICLE AND AN OBJECT RECOGNITION METHOD THEREFOR - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Object Recognition Technology
- 1.13 Original Abstract Submitted
OBJECT RECOGNITION APPARATUS FOR AN AUTONOMOUS VEHICLE AND AN OBJECT RECOGNITION METHOD THEREFOR
Organization Name
Inventor(s)
OBJECT RECOGNITION APPARATUS FOR AN AUTONOMOUS VEHICLE AND AN OBJECT RECOGNITION METHOD THEREFOR - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240338949 titled 'OBJECT RECOGNITION APPARATUS FOR AN AUTONOMOUS VEHICLE AND AN OBJECT RECOGNITION METHOD THEREFOR
Simplified Explanation
The patent application describes an object recognition apparatus and method for an autonomous vehicle. The apparatus includes a processor and storage that corrects distortion and rotation of images to improve object recognition.
- Trains a first network model based on an image
- Corrects distortion and rotation of the image
- Generates distorted images for training
- Trains a feature transfer module based on extracted features
- Inserts the feature transfer module into the network model
- Performs fine-tuning for a second network model
Key Features and Innovation
- Training network models for object recognition
- Correcting image distortion and rotation
- Generating distorted images for training
- Feature transfer module for improved recognition
- Fine-tuning network models for accuracy
Potential Applications
The technology can be used in autonomous vehicles for improved object recognition, leading to enhanced safety and efficiency in navigation.
Problems Solved
The technology addresses the challenges of accurately recognizing objects in varying conditions, such as distortion and rotation in images.
Benefits
- Enhanced object recognition capabilities
- Improved accuracy in identifying objects
- Increased safety and efficiency in autonomous vehicle operations
Commercial Applications
Title: Advanced Object Recognition Technology for Autonomous Vehicles This technology can be utilized in the automotive industry for self-driving cars, drones, and other autonomous systems. It can also be applied in surveillance systems for enhanced security measures.
Prior Art
Readers can explore prior research in the fields of computer vision, machine learning, and autonomous vehicle technology to understand the evolution of object recognition systems.
Frequently Updated Research
Researchers are continually developing new algorithms and techniques to enhance object recognition in autonomous vehicles. Stay updated on advancements in computer vision and artificial intelligence for the latest innovations.
Questions about Object Recognition Technology
How does image distortion affect object recognition accuracy?
Image distortion can make it challenging for algorithms to accurately identify objects, leading to errors in recognition. By correcting distortion and training models with distorted images, the technology improves accuracy.
What are the key components of a feature transfer module?
A feature transfer module extracts features from images and transfers them between different models to enhance recognition capabilities. It plays a crucial role in improving the performance of object recognition systems.
Original Abstract Submitted
an object recognition apparatus and method for an autonomous vehicle are disclosed. the object recognition apparatus includes a processor and storage. the processor: trains a first network model based on an image; corrects distortion and rotation of the image; generates at least one distorted image based on the corrected image; trains a feature transfer module based on a feature extracted from the corrected image and a feature extracted from the at least one distorted image; inserts the feature transfer module into the first network model; and performs fine-tuning for a second network model including the feature transfer module based on the at least one distorted image.
