View source for 18460053. SYSTEMS AND METHODS FOR AN ADAPTIVE AND REGION-SCALE PROPOSING MECHANISM FOR OBJECT RECOGNITION SYSTEMS simplified abstract (Arizona Board of Regents on Behalf of Arizona State University)

=SYSTEMS AND METHODS FOR AN ADAPTIVE AND REGION-SCALE PROPOSING MECHANISM FOR OBJECT RECOGNITION SYSTEMS=


==Organization Name==

[[:Category:Arizona Board of Regents on Behalf of Arizona State University|Arizona Board of Regents on Behalf of Arizona State University]]


[[Category:Arizona Board of Regents on Behalf of Arizona State University]]

==Inventor(s)==

[[:Category:Mohammad Farhadi of Tempe AZ (US)|Mohammad Farhadi of Tempe AZ (US)]][[Category:Mohammad Farhadi of Tempe AZ (US)]]

[[:Category:Yezhou Yang of Scottsdale AZ (US)|Yezhou Yang of Scottsdale AZ (US)]][[Category:Yezhou Yang of Scottsdale AZ (US)]]

[[:Category:Rahul Santhosh Kumar Varma of Tempe AZ (US)|Rahul Santhosh Kumar Varma of Tempe AZ (US)]][[Category:Rahul Santhosh Kumar Varma of Tempe AZ (US)]]

==SYSTEMS AND METHODS FOR AN ADAPTIVE AND REGION-SCALE PROPOSING MECHANISM FOR OBJECT RECOGNITION SYSTEMS - A simplified explanation of the abstract==


This abstract first appeared for US patent application 18460053 titled 'SYSTEMS AND METHODS FOR AN ADAPTIVE AND REGION-SCALE PROPOSING MECHANISM FOR OBJECT RECOGNITION SYSTEMS

==Simplified Explanation==

The abstract describes a pre-processing system and method for reducing the input size of neural network object recognition models based on traffic images characteristics. The system includes a light neural network to detect target regions for further processing and applies a deeper model to those specific regions.

* The system utilizes a light neural network (binary or low precision) to detect target regions in traffic images.
* Once target regions are identified, a deeper neural network model is applied for further processing.
* The system aims to reduce the input size of neural network object recognition models to focus on necessary regions in traffic images.
* Experimentation results on various types of methods, such as conventional convolutional neural networks, transformers, and adaptive models, demonstrate the scalability of the system.

== Potential Applications ==
The technology can be applied in:
* Traffic monitoring systems
* Autonomous vehicles
* Surveillance systems

== Problems Solved ==
* Efficient processing of traffic images
* Reduction of computational resources needed for object recognition
* Improved accuracy in detecting objects in traffic scenes

== Benefits ==
* Faster processing of traffic images
* Enhanced object recognition accuracy
* Reduced computational costs

== Potential Commercial Applications ==
Optimized Traffic Image Processing System for Object Recognition

== Possible Prior Art ==
Prior art in the field of object recognition systems for traffic monitoring and surveillance may exist, but specific examples are not provided in the abstract.

== Unanswered Questions ==
=== How does the system handle variations in lighting conditions in traffic images? ===
The abstract does not mention how the pre-processing system addresses challenges related to different lighting conditions in traffic scenes.

=== What is the impact of the system on real-time processing of traffic images? ===
The abstract does not provide information on the system's performance in real-time applications and its potential latency effects.


==Original Abstract Submitted==

Based on traffic images characteristics, a general pre-processing system and method reduces input size of neural network object recognition models to focus on necessary regions. The system includes a light neural network (binary or low precision; based on configuration) to detect target regions for further processing and applies a deeper model to those specific regions. The present disclosure provides experimentation results on various types of methods, such as conventional convolutional neural networks, transformers, and adaptive models, to show the scalability of the system.



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[[Category:G06V20/54]]