Synthesized Image Generation for Object Detection Learning Data
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Solution Overview
Problem
Existing image recognition technologies face challenges in accurately detecting object regions with limited learning data, often leading to erroneous detections due to the difficulty in covering all object features and learning object features with few textures.
Innovation Solution
A method involving the generation of synthesized images within closed regions of an input image, along with corresponding labels, to create learning data that enhances the detection accuracy of object regions, utilizing a combination of image synthesis and deep learning techniques to improve multi-task detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multi-task detection is used to detect unspecified objects, then the versatility of object detection is improved, but the detection precision deteriorates due to difficulty in covering all object features with limited learning data
Solution Approach 1:
The system performs preliminary actions by generating synthesized learning data before actual detection tasks. The image generation unit creates artificial images with various object textures and appearances in advance, which are then used to train the detection model. This preliminary data preparation enables the model to be pre-adapted to diverse object features, improving both versatility and precision when facing unspecified objects in real detection scenarios.
Solution Approach 2:
The system creates copies of existing object images through synthesis to generate diverse training data. The image generation unit produces multiple variations of object images by manipulating textures, colors, and appearances while preserving the underlying object structures. These synthesized copies serve as additional learning data, enabling the detection model to recognize a broader range of object features without requiring extensive real-world data collection for each object type.
2Device complexity
If learning data is limited, then the complexity of data preparation is reduced, but the detection precision deteriorates due to inability to cover all object features
Solution Approach 1:
The system creates synthetic copies of existing images to expand limited learning data. The image generation unit synthesizes new training images by combining existing image elements with varied textures and appearances, effectively multiplying the information content of the original limited dataset without requiring proportional increases in data collection efforts.
Solution Approach 2:
The system changes parameters of existing images during synthesis, such as texture patterns, color distributions, and appearance characteristics. By manipulating these parameters while preserving the fundamental object structures, the system generates diverse training variations from limited source data, enabling the model to learn robust object features without increasing data preparation complexity.
3Measurement precision
If domain randomization is used to synthesize learning data, then the detection precision for specific objects is improved, but the ability to learn object features with few textures deteriorates
Solution Approach 1:
The system applies local quality by selectively randomizing only certain aspects of image synthesis while preserving others. Specifically, textures and appearances are randomized within local regions or for specific object parts, while the fundamental object structures and geometries remain consistent. This targeted approach enables the model to learn both specific object features and generalizable texture variations, resolving the trade-off between specific detection precision and overall feature learning capability.
Data Source
AI summary
An information processing apparatus comprises a first generation unit configured to generate a synthesized image in which a second image is synthesized in a closed region in a first image, and a second generation unit configured to generate learning data, the learning data including a label and the synthesized image, the label indicating an object region including a region corresponding to the closed region in the synthesized image.