Reference-Based Superpixel Labeling for Accurate Mask Generation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing image-based monitoring systems for tool wear in machining processes are prone to errors due to incorrect or missing labels in superpixels, requiring significant human effort for annotation and limited by the need for large datasets, which hampers efficient tool life prediction and production quality control.
Innovation Solution
A method for generating segmentation masks using superpixels, where labels are automatically assigned based on similarity with reference superpixels, followed by computer-aided correction, reducing human intervention and improving label accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual annotation of superpixels is used for training semantic segmentation models, then label accuracy can be ensured, but significant human effort and time are required
Solution Approach 1:
The patent uses reference superpixels (manually annotated examples) as templates to automatically generate labels for new superpixels by finding similar reference superpixels. This copying approach transfers proven correct labels from reference examples to new instances, ensuring label accuracy while eliminating manual annotation for each new superpixel.
Solution Approach 2:
The system enables automatic self-labeling of superpixels by comparing them against a reference dataset using similarity metrics. The algorithm independently determines labels without human intervention, allowing the system to serve itself by generating training data automatically from the reference superpixels.
2Measurement precision
If large datasets are collected for training semantic segmentation models, then model accuracy improves, but data collection and annotation costs increase
Solution Approach 1:
The patent performs preliminary manual annotation only for a small set of reference superpixels, which are then reused multiple times to automatically generate labels for large numbers of training images. This preliminary action creates a reusable template set that eliminates the need for continuous manual annotation as dataset size grows.
Solution Approach 2:
By copying labels from reference superpixels to numerous new superpixels through similarity matching, the system generates large training datasets from minimal manual effort. Each reference superpixel can serve as a template for multiple instances across different images, exponentially reducing annotation costs.
3Productivity
If automatic labeling of superpixels is implemented, then annotation speed increases, but labeling accuracy decreases due to errors
Solution Approach 1:
The system copies proven correct labels from manually annotated reference superpixels to new superpixels based on similarity. This ensures that automatic labeling inherits the accuracy of manual annotation while achieving high speed, as no new manual labeling is performed.
Solution Approach 2:
The system uses similarity metrics to provide feedback on how well a new superpixel matches reference superpixels. This feedback mechanism ensures that only high-quality matches are used for labeling, maintaining accuracy while enabling automatic processing. The similarity threshold acts as a quality control feedback loop.
Data Source
AI summary
A method for generating a segmentation mask of at least one image comprising generating a plurality of superpixels for the at least one image, automatically generating labels for the generated plurality of the superpixels, wherein the automatically generating labels comprises generating the labels for training a semantic segmentation model based on a plurality of segmentation masks by generating a label for each superpixel by identifying the most similar reference superpixel from a reference data set of reference superpixels, wherein each reference superpixel is associated with a class for supervised training of the semantic segmentation model, wherein the labeled superpixels form the segmentation mask of the at least one image, and computer-aided checking of the generated segmentation mask for correctness, wherein the checking for correctness comprises labeling unlabeled superpixels and correcting labels of incorrectly labeled superpixels by assigning the incorrectly labeled superpixels to the correct class.