RGB-Depth Segmentation Training for Accurate Target Separation
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Solution Overview
Problem
Existing image recognition technologies struggle to accurately segment target objects from images, leading to privacy concerns in facial recognition and product recognition scenarios.
Innovation Solution
A segmentation model training method utilizing a combination of RGB and depth images, involving multiple network models, to enhance the accuracy of target object segmentation by leveraging depth information at different stages of the training process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image recognition is performed on images containing multiple objects, then the system can process real-time data from cameras, but target objects cannot be accurately segmented from background and other objects
Solution Approach 1:
The patent divides the image processing task into multiple independent network models: a first network model for depth feature extraction, a second network model for edge feature extraction, and a third network model for segmentation. This segmentation of functions allows each model to specialize in specific tasks, improving overall segmentation accuracy while managing system complexity through modular architecture.
Solution Approach 2:
The patent combines RGB images with depth images to process information in multiple dimensions. By incorporating depth data as an additional dimension alongside traditional 2D image data, the system achieves more accurate target object segmentation, as depth information provides contextual cues that distinguish targets from background and other objects.
2Measurement precision
If depth information is incorporated into the training process, then segmentation accuracy improves, but the training process becomes more complex
Solution Approach 1:
The training process is segmented into three distinct stages, each involving a specific network model focused on a particular task: depth feature extraction, edge feature extraction, and segmentation. This segmentation of the training process allows depth information to be integrated systematically without overwhelming complexity, as each stage builds upon the previous one in a manageable manner.
Solution Approach 2:
The patent performs preliminary feature extraction from depth images in the first network model before the segmentation task. By pre-processing depth information to extract meaningful features and representations, the system simplifies the subsequent segmentation process, as the third network model receives already-processed depth features rather than raw depth data, reducing overall training complexity.
3Measurement precision
If multiple network models are used for segmentation, then target object identification accuracy improves, but computational resources and time increase
Solution Approach 1:
The patent segments the processing task across three network models that operate in sequence, with each model handling a specific aspect: depth feature extraction, edge feature extraction, and final segmentation. This segmentation allows for specialized processing of different feature types, improving accuracy while the modular structure enables efficient resource allocation and potential parallelization to manage processing time.
Data Source
AI summary
Embodiments of this specification provide a segmentation model training method and apparatus, and an image recognition method and apparatus. The training method includes: obtaining a sample image pair, the sample image pair includes an RGB image and a depth image that are obtained by photographing the same visual range; inputting the depth image into the first network model, to obtain a first depth feature extraction result; inputting a combined image of the depth image and the RGB image into the second network model, to obtain an edge feature of a target object; inputting the edge feature of the target object and the first depth feature extraction result into the third network model, to obtain a segmentation result of the target object; performing a parameter adjustment on the first network model, the second network model, the third network model based on a label and the segmentation result of the target object.