Synthetic Image Generation for Computer Vision Model Training
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Solution Overview
Problem
The generation of annotated training data for machine learning models, particularly in computer vision, is time-consuming and costly, relying heavily on human annotation, which limits the availability of training data and hampers the performance of object detection models.
Innovation Solution
The method involves generating synthetic composite image data by combining segmented foreground and background image data using fully convolutional networks (FCNs) and a spatial transformation layer, with a theta learner that adjusts operations to create images that are difficult for detectors to recognize, thereby increasing the dataset size and improving model performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation methods are used to create training data, then data accuracy is maintained, but time consumption and cost increase significantly
Solution Approach 1:
The patent uses copy-paste augmentation to create synthetic training images by copying existing annotated images and modifying them through transformations (rotation, scaling, cropping, color adjustments). This generates大量 training data without manual annotation while preserving the original annotation accuracy, directly resolving the contradiction between data accuracy and annotation time
Solution Approach 2:
The system performs self-annotation by automatically generating annotations from existing annotated images through geometric transformations and compositing operations. The theta learner model iteratively identifies blind spots and generates hard training examples autonomously, eliminating the need for human annotators while maintaining data quality
2Reliability
If more training data is generated to improve model performance, then detection accuracy improves, but data availability is limited by annotation capacity
Solution Approach 1:
The patent pre-generates a large pool of synthetic training images using copy-paste augmentation before model training. This preliminary data preparation creates abundant training examples that improve model performance without requiring proportional increases in manual annotation capacity
Solution Approach 2:
The theta learner dynamically adapts by iteratively identifying detection blind spots and generating targeted hard training examples. This dynamic process continuously improves model performance by focusing data generation on薄弱环节, maximizing the effectiveness of available training data
3Ease of manufacture
If synthetic image generation is used to increase dataset size, then annotation cost decreases, but image quality and realism may deteriorate
Solution Approach 1:
By copying from existing high-quality annotated images rather than generating entirely synthetic images, the method preserves the photographic realism and quality characteristics of the source images while eliminating annotation costs. The copy-paste approach maintains image quality because it uses real photo segments rather than computer-generated graphics
Solution Approach 2:
The patent merges multiple image segments (foreground objects from one image with backgrounds from another) to create composite training images. This merging process, guided by the theta learner, produces realistic scenes that maintain visual quality while providing diverse training examples at low cost
Data Source
AI summary
Techniques are generally described for object detection in image data. First image data comprising a first plurality of pixel values representing an object and a second plurality of pixel values representing a background may be received. First foreground image data and first background image data may be generated from the first image data. A first feature vector representing the first plurality of pixel values may be generated. A second feature vector representing a first plurality of pixel values of second background image data may be generated. A first machine learning model may determine a first operation to perform on the first foreground image data. A transformed representation of the first foreground image data may be generated by performing the first operation on the first foreground image data. Composite image data may be generated by compositing the transformed representation of the first foreground image data with the second background image data.