Peripheral Ignore Masks for Consistent Dent Detection Training
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Solution Overview
Problem
Existing machine learning models struggle to accurately identify ambiguous image features like dents due to inconsistent annotations and lack of temporal context, leading to false positives and inconsistent training, especially in cases where annotators differ in marking boundaries.
Innovation Solution
A method involving the use of ignore masks, generated based on groundtruth masks, to selectively ignore certain pixels during training, ensuring consistent and accurate learning by focusing on clear annotations and reducing ambiguity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the machine learning model is forced to learn ambiguous dents, then the model becomes more sensitive to detect them, but it detects a lot of false positives
Solution Approach 1:
The patent applies local quality by differentiating the treatment of different pixel regions. Clear pixels receive full training attention while ambiguous pixels are excluded via ignore masks. This allows the model to learn confidently from unambiguous regions without being confused by ambiguous boundary regions, thereby improving detection sensitivity while reducing false positives.
Solution Approach 2:
The training image is segmented into clear pixels and ambiguous pixels using ignore masks. This segmentation allows the loss function to selectively process only clear pixels during training, separating the learning signal from noisy or contradictory annotations. The model thus learns robust features from clear regions without being misled by ambiguous dent boundaries.
2Adaptability or versatility
If annotators mark ambiguous dents differently, then individual preference is accommodated, but the optimizer becomes contradictory and inconsistent
Solution Approach 1:
The patent extracts and removes ambiguous pixels from the training process using ignore masks. By identifying and excluding pixels where annotators show disagreement or uncertainty, the method eliminates contradictory training signals while preserving flexible annotation practices. The optimizer receives consistent gradients only from clear pixels, resolving the contradiction between annotation flexibility and training consistency.
Solution Approach 2:
The ignore mask acts as an intermediary between the annotator's flexible markings and the optimizer's learning process. It filters out ambiguous regions before they can create contradictions, allowing annotators to work freely while ensuring the model receives consistent training signals from the clear pixel subset.
3Productivity
If the model learns insignificant ambiguous dents, then detection coverage increases, but the model detects many false positives
Solution Approach 1:
The patent applies local quality by treating clear and ambiguous pixels differently during training. The model learns to detect dents in clear regions with high confidence while ignoring ambiguous regions that would generate false positives. This selective learning approach maintains detection coverage for significant dents while filtering out noise from insignificant ambiguous features.
Solution Approach 2:
The method applies partial action by training only on clear pixels rather than all pixels including ambiguous ones. This partial training approach is sufficient to learn robust dent detection features without the harmful effect of ambiguous data, achieving good detection coverage while maintaining high reliability by excluding problematic regions.
4Measurement precision
If annotators are provided temporal sequence for reference, then annotation accuracy may improve, but the annotation process becomes more difficult and tedious
Solution Approach 1:
The system applies self-service by automatically generating ignore masks using computer vision algorithms and temporal analysis. Instead of requiring annotators to manually review temporal sequences or make complex decisions about ambiguous boundaries, the automated system identifies and masks ambiguous regions, allowing annotators to focus only on clear markings and reducing the overall annotation complexity.
Data Source
AI summary
A method of training a machine learning model to identify image features including providing training image data including pixels, assigning a groundtruth annotation to each pixel relating to a respective pixel and each groundtruth annotation indicating whether or not that the pixel corresponds with an image feature, providing an ignore mask including a set of ignore flags relating to a respective pixel and each ignore flag providing an indication that the pixel should be ignored, for each pixel, receiving a prediction value from the machine learning model indicating a probability of the pixel corresponding with an image feature, for each pixel which has no ignore flag, determining a loss value based on the prediction value and groundtruth annotation for that pixel, and training the machine learning model based on the loss value, and for each pixel having an ignore flag, ignoring the prediction value for that pixel.