Multi-Loss Image Segmentation Training for Continuous Linear Objects
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Solution Overview
Problem
Existing methods fail to accurately and efficiently evaluate the continuity and indefinite shapes of linear-shaped objects in image recognition, particularly in semantic segmentation.
Innovation Solution
A learning apparatus that employs multiple loss functions to evaluate and optimize a model for image segmentation, including a first loss function for identifying features, a second loss function for evaluating continuity, and optionally a third loss function for emphasizing contours, through backpropagation, to generate a model that accurately recognizes linear-shaped objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If semantic segmentation using deep learning is used to improve recognition resolution for linear objects, then the resolution of recognition is improved, but the ability to accurately evaluate continuity and indefinite shapes deteriorates
Solution Approach 1:
The loss function is segmented into multiple independent components: a first loss function for basic segmentation accuracy, a second loss function for continuity evaluation, and a third loss function for contour emphasis. Each component addresses a specific aspect of linear object recognition, allowing the model to optimize each feature separately while maintaining overall performance.
Solution Approach 2:
The patent changes the parameters of the loss function by introducing multiple evaluation metrics with different mathematical formulations. The second loss function uses element product calculations to evaluate continuity, while the third loss function applies contour weighting to emphasize boundary features. These parameter changes enable the model to simultaneously optimize for resolution, continuity, and shape accuracy.
2Device complexity
If a single loss function is used for model training, then the training process is simple, but the model cannot accurately evaluate multiple features simultaneously
Solution Approach 1:
Multiple loss functions are merged into a unified training framework where the total loss is the sum of individual loss components. This allows the model to evaluate multiple features (segmentation accuracy, continuity, contour definition) simultaneously while maintaining a coherent training process. The gradient from each loss function is backpropagated together to update the model parameters.
Solution Approach 2:
The loss function system is designed to be multi-functional, where a single training mechanism serves multiple purposes: basic segmentation, continuity evaluation, and contour emphasis. This universal approach allows one training process to optimize multiple aspects of linear object recognition without requiring separate training procedures for each feature.
3Productivity
If conventional loss functions are used for linear object recognition, then the training is efficient, but the model fails to capture continuity features of linear shapes
Solution Approach 1:
The second loss function acts as an intermediary between the basic segmentation loss and the final output, specifically evaluating the continuity feature. It calculates element products of segmentation masks at different positions to determine how well the model preserves the continuous nature of linear objects. This intermediary evaluation guides the model to maintain continuity without disrupting the overall training efficiency.
Data Source
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AI summary
A learning apparatus configured to train a model in an image segmentation apparatus configured to perform segmentation of a recognition target in an image using the model, the learning apparatus including: a first evaluation value acquisition unit configured to acquire a first evaluation value calculated by a first loss function configured to evaluate a feature configured to identify the recognition target in the image; a second evaluation value acquisition unit configured to acquire a second evaluation value calculated by a second loss function configured to evaluate a continuity of the recognition target identified in the image; and a learning execution unit configured to perform learning to optimize the model by performing backpropagation based on the first evaluation value and the second evaluation value.