Mask R-CNN Self-Training for Robust Image Segmentation
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Solution Overview
Problem
Traditional auto-labeling and segmentation tools face limitations due to a lack of diverse data in training datasets, requiring human supervision and being costly to generate, and are not robust to environmental variations like rotation and occlusion.
Innovation Solution
A two-stage self-training method using Mask R-CNN models to generate segmentation masks and bounding boxes, which can be rotated and modified to create a more robust training dataset, reducing the need for human intervention and domain gap issues.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional auto-labeling tools use existing training datasets, then the tools can be developed, but they lack robustness for diverse applications due to insufficient data in different domains
Solution Approach 1:
The system performs self-training by automatically generating training data and annotations through a two-stage process: first generating pseudo-labels using a pre-trained model, then using these pseudo-labels to retrain and improve the model. This self-service mechanism eliminates the need for manual annotation while adapting to diverse domains without requiring pre-existing domain-specific datasets.
Solution Approach 2:
The system performs preliminary actions by first generating pseudo-labels and segmentation masks using a pre-trained model before actual training occurs. These preliminary annotations are then used to create augmented training datasets that prepare the model for robust performance in diverse domains, including handling rotation and occlusion cases.
2Measurement precision
If human annotations are used for training datasets, then the data quality is high, but the process is time consuming and error prone
Solution Approach 1:
The system replaces manual human annotation with automated self-training. A pre-trained model generates pseudo-labels and segmentation masks automatically, which are then used to retrain the model. This self-service approach maintains high annotation quality while eliminating the time-consuming human intervention process.
Solution Approach 2:
The system implements feedback by using the pseudo-labels generated in the first stage as training data for the second stage. This feedback loop allows the model to continuously improve its annotation quality through iterative retraining on its own generated data, achieving high precision without human time investment.
3Reliability
If CNNs use rectangular boxes with edges parallel to image boundaries, then the model performance is optimized, but this creates a domain gap in real-world applications with rotated objects
Solution Approach 1:
The system performs preliminary data augmentation by rotating training images and objects before training occurs. This preliminary action ensures the model learns to handle rotated objects from the start, eliminating the domain gap between rectangular box assumptions and real-world rotated objects without compromising optimized model performance.
Solution Approach 2:
The system changes the parameter of object orientation in the training data by applying random rotations to images and their corresponding annotations. This parameter change allows the model to learn rotational invariance while maintaining its optimized performance characteristics, bridging the gap between rectangular box constraints and real-world rotational variability.
4Reliability
If new training datasets are generated to improve robustness, then the model can handle environmental variations, but the cost and complexity of data generation increases
Solution Approach 1:
The system uses self-service by having the model generate its own training data through a two-stage process: generating pseudo-labels with a pre-trained model, then using these pseudo-labels to retrain. This eliminates the need for complex manual data collection and annotation processes while achieving robustness to environmental variations like rotation and occlusion.
Solution Approach 2:
The system creates copies of existing training data by generating synthetic annotations and augmented images through automated processes. These copies replicate the complexity of real-world scenarios including rotated and occluded objects, providing robust training data without requiring expensive manual creation processes.
Data Source
AI summary
In various examples, training methods as described to generate a trained neural network that is robust to various environmental features. In an embodiment, training includes modifying images of a dataset and generating boundary boxes and/or other segmentation information for the modified images which is used to train a neural network.