18241258. OBJECT DETECTION SYSTEM AND METHOD simplified abstract (Fujitsu Limited)
Contents
- 1 OBJECT DETECTION SYSTEM AND METHOD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 OBJECT DETECTION SYSTEM AND METHOD - 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 Unanswered Questions
- 1.11 Original Abstract Submitted
OBJECT DETECTION SYSTEM AND METHOD
Organization Name
Inventor(s)
OBJECT DETECTION SYSTEM AND METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 18241258 titled 'OBJECT DETECTION SYSTEM AND METHOD
Simplified Explanation
The abstract describes an object-detection system utilizing machine learning to detect objects in images. The system includes an edge computer to extract features from reduced images and a server to decode and perform object-detection on divided features associated with divided images.
- Edge computer extracts features from reduced images and transmits them.
- Server decodes features and performs object-detection on divided features.
- Object-detection model is divided between edge computer and server.
Potential Applications
This technology can be applied in various fields such as surveillance, autonomous vehicles, robotics, and image recognition systems.
Problems Solved
1. Efficient object detection in images. 2. Handling large image datasets effectively.
Benefits
1. Improved accuracy in object detection. 2. Faster processing of images. 3. Reduced computational load on individual devices.
Potential Commercial Applications
"Object-Detection System for Efficient Image Analysis in Surveillance and Autonomous Vehicles"
Possible Prior Art
One possible prior art could be the use of distributed computing systems for image processing tasks. Another could be the use of machine learning models for object detection in images.
Unanswered Questions
How does the system handle real-time object detection in dynamic environments?
The abstract does not provide details on the system's performance in real-time scenarios with moving objects.
What is the scalability of the system for processing large volumes of images?
The abstract does not mention the system's scalability for handling a high volume of images simultaneously.
Original Abstract Submitted
An object-detection system configured to form an object-detection model generated by machine learning to detect an object from an image, the object-detection system includes an edge computer configured to extract a feature from a reduced image in which an input image is reduced to a predetermined size, and compress and transmit the feature, and a server configured to decode the feature, and perform object-detection for each of divided features into which the feature is divided in association with respective divided images into which the reduced image is divided with overlapping regions in a first size, the divided features including a second size that depends on a division position in the object-detection model, wherein the predetermined size is determined based on the first size of the overlapping regions and the second size of the divided features, and wherein the object-detection model is divided into the edge computer and the server.
