20230162373. MOVING TARGET FOCUSING METHOD AND SYSTEM BASED ON GENERATIVE ADVERSARIAL NETWORK simplified abstract (University of Electronic Science and Technology of China)
MOVING TARGET FOCUSING METHOD AND SYSTEM BASED ON GENERATIVE ADVERSARIAL NETWORK
Organization Name
University of Electronic Science and Technology of China
Inventor(s)
MOVING TARGET FOCUSING METHOD AND SYSTEM BASED ON GENERATIVE ADVERSARIAL NETWORK - A simplified explanation of the abstract
This abstract first appeared for US patent application 20230162373 titled 'MOVING TARGET FOCUSING METHOD AND SYSTEM BASED ON GENERATIVE ADVERSARIAL NETWORK
Simplified Explanation
The patent application describes a method and system for focusing on moving targets using a generative adversarial network (GAN). Here are the key points:
- The method involves generating a two-dimensional image with defocused moving targets using a range doppler algorithm.
- Ideal Gaussian points are generated at the center of each defocused moving target in the image to create training labels.
- A generative adversarial network (GAN) is constructed, consisting of a generative network and a discrimination network.
- The training sample and training labels are inputted into the GAN for repeated training until the generative network meets a preset condition, resulting in a trained network model.
- The trained network model can then be used to input a testing sample and output a focused image of the moving target.
Potential applications of this technology:
- Surveillance systems: Enhancing the focus on moving targets in surveillance footage.
- Autonomous vehicles: Improving the ability to detect and focus on moving objects in real-time.
- Medical imaging: Enhancing the focus on moving targets in ultrasound or MRI scans.
- Robotics: Improving object detection and tracking capabilities in robotic systems.
Problems solved by this technology:
- Traditional methods struggle to focus on moving targets accurately, resulting in blurred or distorted images.
- Manual adjustment of focus parameters is time-consuming and may not be feasible in real-time scenarios.
- Existing algorithms may not effectively handle defocused moving targets, leading to inaccurate results.
Benefits of this technology:
- Improved accuracy: The use of a generative adversarial network allows for more accurate focusing on moving targets.
- Real-time capability: The trained network model can provide focused images in real-time, enabling faster decision-making.
- Automation: The system eliminates the need for manual adjustment of focus parameters, reducing human intervention.
- Versatility: The technology can be applied to various domains, including surveillance, autonomous vehicles, medical imaging, and robotics.
Original Abstract Submitted
a moving target focusing method and system based on a generative adversarial network are provided. the method includes: generating, using a range doppler algorithm, a two-dimensional image including at least one defocused moving target, as a training sample; generating at least one ideal gaussian point in a position of at least one center of the at least one defocused moving target in the two-dimensional image, to generate a training label; constructing the generative adversarial network, the generative adversarial network includes a generative network and a discrimination network; inputting the training sample and the training label into the generative adversarial network to perform repeated training until an output of the generative network reaches a preset condition, to thereby obtain a trained network model; and inputting a testing sample into the trained network model, to output a moving target focused image.
