View source for 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))

=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==

[[:Category:Telefonaktiebolaget LM Ericsson (publ)|Telefonaktiebolaget LM Ericsson (publ)]]


[[Category:Telefonaktiebolaget LM Ericsson (publ)]]

==Inventor(s)==

[[:Category:Fredrik Dahlgren of Lund (SE)|Fredrik Dahlgren of Lund (SE)]][[Category:Fredrik Dahlgren of Lund (SE)]]

[[:Category:Anders Berkeman of Lund (SE)|Anders Berkeman of Lund (SE)]][[Category:Anders Berkeman of Lund (SE)]]

==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 ().



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