Neural Image Object Detection With Synthetic Training Augmentation
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Solution Overview
Problem
Conventional object detection systems in images require manual configuration and large training datasets, are application-specific, and struggle with accuracy in varying contexts, especially when objects are in unusual locations or layouts.
Innovation Solution
A neural network-based system that augments base images with synthetically generated objects at varied locations and qualities, allowing efficient detection and classification of multiple object classes and types using a small number of training images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object detection systems use manual configuration and large training datasets, then detection accuracy may be improved, but system complexity and time required for setup increase significantly
Solution Approach 1:
The patent uses synthetic copies of objects generated from 3D models or images to create training data. These synthetic objects replicate real objects' visual characteristics but can be generated in unlimited variations, eliminating the need for manual collection of diverse real object images and reducing dependency on large real-world datasets.
Solution Approach 2:
The system automatically adjusts training parameters by varying synthetic object parameters (position, orientation, lighting, background) to create diverse training scenarios. This automated parameter variation replaces manual configuration efforts and enables the system to adapt to different detection contexts without human intervention.
2Measurement precision
If conventional systems use application-specific detection methods, then detection accuracy for specific tasks may improve, but adaptability to different contexts and applications decreases
Solution Approach 1:
The patent creates a universal object detection system that can detect multiple object classes and types using a single trained model. The system handles diverse objects (barcodes, logos, text, products) across different contexts without requiring separate application-specific models, achieving both high accuracy and broad adaptability.
Solution Approach 2:
The detection task is segmented into independent object detection problems, each handled by the same unified model through separate detection heads or classification branches. This segmentation allows the system to maintain specialized detection capabilities for different object types while sharing common processing infrastructure, enabling context adaptability.
3Adaptability or versatility
If objects are detected in unusual locations or layouts, then system versatility improves, but detection accuracy decreases for conventional systems
Solution Approach 1:
The patent employs dynamic detection capabilities where the system automatically adjusts its detection parameters and decision boundaries based on the spatial distribution and layout characteristics of objects in the input image. This dynamic adaptation allows the system to maintain high accuracy regardless of whether objects appear in conventional or unusual locations.
Solution Approach 2:
The system uses feedback from synthetic training data that includes various object positions and layouts to continuously refine detection algorithms. The training process incorporates examples of objects in unusual locations, enabling the system to learn and adapt to diverse spatial arrangements while maintaining detection accuracy through iterative optimization.
4Measurement precision
If extensive training data is used, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
By generating synthetic training data from 3D models or a small set of real images, the system creates unlimited training examples without the computational cost of processing large volumes of real images. This synthetic data approach provides comprehensive training coverage with minimal computational resources during the training phase.
Solution Approach 2:
The system performs preliminary training on synthetic data that pre-condition the detection model on various object positions, orientations, and contexts. This preliminary training on simplified synthetic examples prepares the model for real-world detection tasks, reducing the need for extensive real data processing and enabling faster inference.
Data Source
AI summary
Aspects of the disclosure provide for mechanisms for identification of objects in images using neural networks. A method of the disclosure includes: obtaining an image, representing each element of a plurality of elements of the image via an input vector of a plurality of input vectors, each input vector having one or more parameters pertaining to visual appearance of a respective element of the image, providing the plurality of input vectors to a first subnetwork of a neural network to obtain a plurality of output vectors, wherein each of the plurality of output vectors is associated with an element of the image, identifying, based on the plurality of output vectors, a sub-plurality of elements of the image as belonging to the image of the object, and determining, based on locations of the sub-plurality of elements, a location of an image of an object within the image.