Neural Network Image Segmentation for Multi-Scale Object Detection
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Solution Overview
Problem
Neural networks struggle with accurately identifying objects in images with varying sizes and perspectives due to their lack of specific training for such scenarios, leading to inaccurate object detection.
Innovation Solution
The system splits images into multiple portions, applies neural networks to each portion, and combines the results to enhance object detection accuracy, using techniques like super sampling neural networks for resolution adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are used to identify objects from images containing many objects with different sizes, then object detection can be performed, but detection accuracy deteriorates due to size variation and perspective differences
Solution Approach 1:
The patent divides the image into multiple overlapping regions or crops at different scales and feeds them separately to the neural network. This segmentation approach allows the network to focus on specific regions with objects of particular sizes, improving detection accuracy for objects of varying scales and perspectives that would otherwise be missed in a single full-image processing approach.
2Device complexity
If a single neural network model is used for object detection, then processing is simple, but detection accuracy for objects of different sizes deteriorates
Solution Approach 1:
The patent dynamically adjusts the processing approach by creating multiple scaled versions or crops of the input image and processing them through the same neural network model. This dynamic adaptation allows a single static network model to effectively handle objects of varying sizes by presenting it with appropriately scaled inputs, maintaining model simplicity while improving detection accuracy across different object scales.
Data Source
AI summary
Apparatuses, systems, and techniques to identify objects with in an image irrespective of a size or perspective of the objects with which the one or more neural networks are trained. In at least one embodiment, objects are identified in an image using one or more neural networks, in which the image is split into a multiple sections and one or more neural networks are used to detect objects in every section.