18033226. A Computer Software Module Arrangement, a Circuitry Arrangement, an Arrangement and a Method for Improved Object Detection Adapting the Detection through Shifting the Image simplified abstract (Telefonaktiebolaget LM Ericsson (publ))
Contents
- 1 A Computer Software Module Arrangement, a Circuitry Arrangement, an Arrangement and a Method for Improved Object Detection Adapting the Detection through Shifting the Image
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 A Computer Software Module Arrangement, a Circuitry Arrangement, an Arrangement and a Method for Improved Object Detection Adapting the Detection through Shifting the Image - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Original Abstract Submitted
A Computer Software Module Arrangement, a Circuitry Arrangement, an Arrangement and a Method for Improved Object Detection Adapting the Detection through Shifting the Image
Organization Name
Telefonaktiebolaget LM Ericsson (publ)
Inventor(s)
A Computer Software Module Arrangement, a Circuitry Arrangement, an Arrangement and a Method for Improved Object Detection Adapting the Detection through Shifting the Image - A simplified explanation of the abstract
This abstract first appeared for US patent application 18033226 titled 'A Computer Software Module Arrangement, a Circuitry Arrangement, an Arrangement and a Method for Improved Object Detection Adapting the Detection through Shifting the Image
Simplified Explanation
The abstract describes an object detection arrangement that uses a multi-scale convolutional neural network to detect objects in images. The arrangement includes a controller that receives image data and determines the distance of the object to be detected within the image. Based on this distance, the controller classifies whether there is a risk of incorrectly detecting the object. If there is a risk, the controller compensates for it by shifting the image.
- The object detection arrangement uses a multi-scale convolutional neural network for detecting objects in images.
- The controller receives image data and determines the distance of the object to be detected within the image.
- Based on the distance, the controller classifies whether there is a risk of incorrectly detecting the object.
- If there is a risk, the controller compensates for it by shifting the image.
Potential Applications
- Object detection in autonomous vehicles to improve accuracy and reliability.
- Surveillance systems for detecting and tracking objects in real-time.
- Robotics applications for object recognition and manipulation.
Problems Solved
- Reduces the risk of incorrectly detecting objects based on their distance in the image.
- Improves the accuracy of object detection in various applications.
- Provides a mechanism for compensating and adjusting the object detection process.
Benefits
- Enhanced object detection accuracy by considering the distance of the object in the image.
- Improved reliability and performance of object detection systems.
- Increased efficiency in object recognition and tracking tasks.
Original Abstract Submitted
An object detection arrangement () comprising a controller () configured to detect objects utilizing a multi-scale convolutional neural network, wherein the controller () is further configured to: receive () image data representing an image () comprising an object to be detected () being at a distance (d) into the image (); classify () whether the object to be detected () is at risk of being incorrectly detected based on the distance (d); and if so compensate () the object detection by shifting () the image ().
