Multistage Neural Network for High-Resolution Image Object Detection
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Solution Overview
Problem
Conventional image classification systems face challenges in efficiently processing high-resolution images and adapting to new use cases due to limitations in memory, processing, and system resources, making them inefficient for robust neural network operations and real-time object detection.
Innovation Solution
A neural network target classification system that includes a proposal component, a multistage neural network, and an attention coordinator to optimize resource allocation and detect objects in images by determining regions of interest and assigning them to appropriate neural networks, allowing for efficient processing of high-resolution images and video streams.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a robust trained neural network is implemented for real-time object detection, then detection accuracy and reliability are improved, but system resource consumption (memory, processing power) increases
Solution Approach 1:
The patent divides the image processing task into multiple stages: a first neural network processes the entire image to identify candidate regions, then a second neural network processes only those candidate regions for final classification. This segmentation allows the system to use a larger, more accurate neural network architecture without processing the entire image at full resolution, thereby improving detection accuracy while controlling resource consumption.
Solution Approach 2:
The patent employs a multi-scale approach where the same neural network architecture is applied at different image resolutions. A first neural network operates on a lower-resolution version of the image to identify candidate regions, then a second neural network processes higher-resolution candidate regions. This dimensional approach enables the system to achieve robust detection performance while managing computational resources efficiently across different processing stages.
2Device complexity
If a single neural network processes all images, then system complexity is reduced, but processing speed and efficiency decrease
Solution Approach 1:
The patent divides the image processing task into multiple stages: a first neural network processes the entire image to identify candidate regions, then a second neural network processes only those candidate regions for final classification. This segmentation allows the system to use a larger, more accurate neural network architecture without processing the entire image at full resolution, thereby improving detection accuracy while controlling resource consumption.
Solution Approach 2:
The patent implements a two-stage processing approach where the first neural network performs a coarser analysis to identify candidate regions, and only those specific regions are processed by the second neural network. This partial processing approach means that not all image data is processed at full detail, significantly improving processing speed while maintaining detection accuracy for regions of interest.
3Measurement precision
If high-resolution images are processed in full detail, then object detection precision is improved, but computational processing time increases
Solution Approach 1:
The patent divides the image processing task into multiple stages: a first neural network processes the entire image to identify candidate regions, then a second neural network processes only those candidate regions for final classification. This segmentation allows the system to use a larger, more accurate neural network architecture without processing the entire image at full resolution, thereby improving detection accuracy while controlling resource consumption.
Solution Approach 2:
The patent employs a multi-scale approach where the same neural network architecture is applied at different image resolutions. A first neural network operates on a lower-resolution version of the image to identify candidate regions, then a second neural network processes higher-resolution candidate regions. This dimensional approach enables the system to achieve robust detection performance while managing computational resources efficiently across different processing stages.
Data Source
AI summary
High resolution image target classification systems and methods include a proposal component configured to receive a first set data associated with a scene, the first set of data including at least one image of the scene, a multistage neural network comprising a plurality of neural networks, each neural network trained to receive a region of interest and output an object classification in accordance with an associated resource allocation, and an attention coordinator configured to determine regions of interest in the image and allocate each determined region to one of the plurality of neural networks from the multi-scale neural network, in accordance with available system resources. The system may be configured to optimize a probability of detecting objects in the image, while minimizing a number of pixels processed through the multi-scale neural network.