Neural Network System Adaptive Selection for Image Processing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image processing methods are computationally intensive for detecting characteristics of images, necessitating more efficient approaches.
Innovation Solution
A method and system utilizing a selection between two neural network systems, one with fewer layers and fewer neurons and interconnections, and another more complex system, to process image data based on triggers such as resource availability or detection performance, allowing for adaptive processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a more complex neural network system with more layers, neurons, and interconnections is used, then detection accuracy is improved, but computational load and processing time increase
Solution Approach 1:
The system dynamically selects between a first neural network system (with fewer layers, neurons, and interconnections) and a second neural network system (with more layers, neurons, and interconnections) based on trigger conditions. This allows the system to adapt its complexity level in real-time, using simpler models when computational resources are constrained and more complex models when accuracy is prioritized, thereby resolving the contradiction between detection accuracy and processing efficiency
Solution Approach 2:
The invention changes the parameter of neural network complexity by switching between different network configurations. The system monitors trigger conditions (such as computational resource availability or detection performance requirements) and adjusts the network complexity parameter accordingly, selecting from multiple pre-configured neural network systems with varying numbers of layers, neurons, and interconnections to optimize the balance between accuracy and efficiency
2Productivity
If a simpler neural network system with fewer layers, neurons, and interconnections is used, then processing efficiency is improved, but detection accuracy decreases
Solution Approach 1:
The system dynamically adapts its model complexity based on real-time trigger conditions. When computational resources are limited or processing speed is prioritized, the system selects the first neural network system with fewer layers, neurons, and interconnections to maintain high processing efficiency. When detection accuracy becomes the priority, the system switches to the second neural network system with more complex architecture, thereby resolving the trade-off between efficiency and accuracy through dynamic adaptation
3Adaptability or versatility
If multiple neural network systems are maintained and switched between, then adaptability to different processing conditions is improved, but device complexity increases
Solution Approach 1:
The invention segments the neural network systems into distinct configurations (first neural network system with simpler architecture and second neural network system with more complex architecture). Each segment is optimized for specific processing conditions, and the system switches between these pre-defined segments based on triggers. This segmentation approach allows the system to achieve high adaptability to different processing conditions while managing complexity through modular, pre-configured options rather than a single monolithic complex system
Data Source
AI summary
A method of processing image data representative of at least part of an image using a computing system to detect at least one class of object in the image. The method comprises processing the image data using a neural network system selected from a plurality of neural network systems including a first neural network system arranged to detect a class of objects, and a second neural network system arranged to detect the class of objects. The first neural network system comprises a first plurality of layers and the second neural network system comprises a second plurality of layers. The second neural network system has at least one of: more layers than the first neural network system; more neurons than the first neural network system; and more interconnections between neurons than the first neural network system. The method comprises obtaining a trigger and, on the basis of the trigger, processing the image data using a selected one of the first and second neural network systems.