View source for Hyundai motor company (20240338949). OBJECT RECOGNITION APPARATUS FOR AN AUTONOMOUS VEHICLE AND AN OBJECT RECOGNITION METHOD THEREFOR simplified abstract

=OBJECT RECOGNITION APPARATUS FOR AN AUTONOMOUS VEHICLE AND AN OBJECT RECOGNITION METHOD THEREFOR=


==Organization Name==

[[:Category:hyundai motor company|hyundai motor company]]


[[Category:hyundai motor company]]

==Inventor(s)==

[[:Category:Jong Hyun Choi of Seoul (KR)|Jong Hyun Choi of Seoul (KR)]][[Category:Jong Hyun Choi of Seoul (KR)]]

==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.



[[Category:G06V20/58]]
[[Category:G06V10/24]]
[[Category:G06V10/48]]
[[Category:G06V10/82]]
[[Category:CPC_G06V20/58]]