Dimensionally Aware Neural Network for Multi-Scale Object Localization
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Solution Overview
Problem
Current machine learning systems face challenges in accurately predicting object locations in images, particularly when objects are at varying scales and distances, due to limitations in handling dimensional awareness and scale variability.
Innovation Solution
The development of a Dimensionally Aware Neural Network system that generates a plurality of variant images by re-scaling input images with different scaling factors, allowing the system to make scale-selective predictions and determine object locations based on in-scope and out-of-scope image scales.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single-scale machine learning model is used, then the device complexity is low, but the measurement precision of object locations deteriorates when objects are at varying scales
Solution Approach 1:
The system segments the object detection task by creating multiple neural network models, each specialized for detecting objects at specific scales. Instead of using one general model, the system divides detection into scale-specific sub-tasks, with each model trained on images containing objects of particular size ranges, thereby improving precision for each scale segment.
Solution Approach 2:
The system adds a scale dimension to the detection framework by generating multiple scaled versions of input images and processing them through different specialized models. This transforms a single-scale detection problem into a multi-scale detection system, where each model operates in its optimal scale dimension.
2Measurement precision
If multiple scaled images are generated and processed, then the measurement precision of object locations improves, but the productivity of the system deteriorates due to increased processing time
Solution Approach 1:
The system performs preliminary actions by pre-processing images into multiple scaled versions before detection. These scaled images are generated in advance and organized into scale-specific groups, so that when detection is needed, the system can directly apply the appropriate pre-prepared models without real-time scaling operations.
Solution Approach 2:
Different regions of the input image are processed at different scales based on local characteristics. The system identifies regions containing objects of specific scales and applies the corresponding specialized model to that local region, rather than processing the entire image through all models.
3Manufacturing precision
If scale-selective neural networks are used, then the manufacturing precision of object detection improves, but the ease of operation deteriorates due to multiple models required
Solution Approach 1:
The system creates a universal detection framework that handles multiple scales through a standardized multi-model architecture. Despite using multiple specialized models, the system presents a unified interface and automated selection process, making it as easy to operate as a single-model system while achieving superior multi-scale detection accuracy.
Data Source
AI summary
In an aspect, the present disclosure provides a method of providing a dimensionally aware prediction for an object in an image captured by an image sensor, using a scale selective machine learning system, comprising: obtaining an input comprising image data of an object at an input image scale; generating a plurality of variant images based on re-scaling the input with a plurality of different image scaling factors, each variant image comprising the object at a variant image scale; generating a plurality of scale selective predictions based on the plurality of variant images, and assigning an in-scope response when the variant image comprises the object at an in-scope image scale, and determining a location prediction for the object based on a scale selective prediction comprising the in-scope response.