Deep Learning Model for Multi-Object Image Segmentation
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Solution Overview
Problem
Deep learning models require extensive training and computational resources when segmenting multiple regions in images, as each region necessitates separate training data and models, making it inefficient for segmenting a large number of objects simultaneously.
Innovation Solution
A method using a single deep learning model that generates probability maps indicating the likelihood of pixels belonging to combinations of objects, allowing for the segmentation of multiple objects by defining combinations of regions and minimizing a loss function based on true masks, thereby reducing the number of required probability maps and training time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple deep learning models are used to segment multiple regions, then segmentation accuracy for each region is improved, but device complexity and training time increase
Solution Approach 1:
The patent combines multiple separate deep learning models into a single unified model that processes multiple regions simultaneously. The model uses a shared backbone network for feature extraction and multiple output heads for different regions, reducing the total number of models from N to 1 while maintaining segmentation accuracy for all regions.
Solution Approach 2:
The single deep learning model is designed to perform multiple segmentation functions for different regions within the same image. The model architecture includes region-specific output layers that enable it to segment multiple types of objects or regions using a single universal model, eliminating the need for multiple specialized models.
2Measurement precision
If separate training data is prepared for each region, then segmentation quality for each region is improved, but loss of time and training resources increase
Solution Approach 1:
The training process merges multiple region-specific training tasks into a single unified training procedure. The model is trained on combined training data that includes annotations for all regions simultaneously, using a unified loss function that aggregates errors from all regions. This allows the model to learn all region segmentations in one training run rather than requiring separate training processes for each region.
Solution Approach 2:
The model performs preliminary feature extraction that is shared across all regions before final region-specific segmentation. By extracting common features once and then applying region-specific processing, the training time is reduced while maintaining the quality needed for each individual region.
3Adaptability or versatility
If multiple deep learning models are trained for multiple objects, then comprehensive object segmentation is achieved, but computational load and training resources increase
Solution Approach 1:
The patent merges multiple object segmentation tasks into a single computational process. The unified model shares computational resources such as feature extraction layers across all object types, performing only one forward pass per image regardless of the number of objects to be segmented. This reduces the total computational load compared to running multiple separate models.
Solution Approach 2:
The single deep learning model is designed with multi-functional output capabilities that enable it to segment various types of objects within the same image. The model architecture includes multiple output channels or heads that correspond to different object classes or regions, allowing one model to replace multiple specialized models and reduce overall computational requirements.
Data Source
AI summary
An image segmentation method and apparatus are provided. The image segmentation apparatus inputs an image into a deep learning model to obtain a plurality of probability maps, and, based on the plurality of probability maps, identifies a plurality of objects in the image. Here, the plurality of probability maps include prediction values of the deep learning model indicating a probability that each pixel of the image belongs to a plurality of combinations defined as regions of at least two objects among the plurality of objects.