Object Detection Model Adaptation for Image Domain Shift
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Solution Overview
Problem
Existing domain adaptation methods for object detection models are ineffective in addressing domain shift due to image corruptions, requiring laborious re-training and relying on clean labeled data, synthetic augmentations, and noisy pseudo-labels, which limits their performance improvement.
Innovation Solution
A method involving a gradual adaptation approach that generates mixed image samples using pseudo-labels and batch normalization adjustments, first adapting only batch normalization parameters and then fine-tuning all layers, to enhance model robustness to domain shift.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the trained object detection model is deployed to computing systems operating in real-environment with new data from different domains, then the model can be applied to various locations and environments, but the performance degrades due to domain shift and distribution differences between training data and new data
Solution Approach 1:
The patent applies preliminary action by performing domain adaptation training before the model is deployed to target domains. The method pre-trains the model on source domain data and then adapts it to target domain data distributions, preparing the model in advance to handle domain shift issues when deployed to different locations and environments, thereby maintaining high performance without requiring retraining for each new domain
2Adaptability or versatility
If the trained model is exposed to corrupted digital images with low-level image corruptions, then the model can process images from various conditions, but the model fails to detect objects or classifies them incorrectly due to inability to handle corruptions
Solution Approach 1:
The patent converts the harmful effect of image corruptions into a beneficial training opportunity by using corrupted images as training data. The method trains the model on corrupted images from the target domain, transforming the previously harmful corruptions into useful training examples that teach the model to robustly handle various image conditions, thereby improving detection accuracy on corrupted images
3Adaptability or versatility
If the trained model is deployed in many locations with varying environmental conditions such as different light, shade, rain, fog, wind, snow, sunlight, geometric positions, and visual characteristics, then the model achieves broad coverage, but the accuracy decreases due to significant domain shift and visual characteristic differences
Solution Approach 1:
The patent applies parameter changes by adapting the model's internal parameters (weights and biases) through domain adaptation training. The method modifies the model parameters to account for variations in environmental conditions such as light, shade, weather, and geometric positions, enabling the model to maintain high detection accuracy across diverse locations without requiring manual adjustment for each environment
4Reliability
If the trained model is re-trained with additional labeled images from all variations of target domains, then the model performance improves on new images, but the process becomes laborious and expensive due to the requirement to annotate new images with labels
Solution Approach 1:
The patent uses copying by leveraging the pre-trained model's knowledge to generate pseudo-labels for target domain images. Instead of manually annotating all target domain images, the method copies the source domain training data and adapts it to the target domain using the pre-trained model's predictions, significantly reducing the labor required for annotation while maintaining training effectiveness
5Adaptability or versatility
If the trained model uses synthetic augmentations and noisy pseudo-labels for domain adaptation, then the model can be adapted to target domains, but the performance improvement is limited due to the quality and reliability of the training data
Solution Approach 1:
The patent implements feedback by using the pre-trained model's predictions on target domain images to generate pseudo-labels that are then used for adaptation training. The method continuously refines the adaptation process by feedback from the model's own predictions, improving the quality of pseudo-labels and enhancing performance improvement compared to using only synthetic augmentations or noisy labels
Data Source
AI summary
The present disclosure provides a method and system for adapting a machine learning model, such as an object detection model, to account for domain shift. The method includes receiving a labeled data elements and target image samples and performing a plurality of model adaptation epochs. Each adaptation epoch includes: predicting for each of the target image samples, using the machine learning model configured by a current set of configuration parameters, a corresponding target class label for the respective target data object included in the target image sample; generating a plurality of labeled mixed data elements that each include: (i) a mixed image sample including a source data object from one of the source image samples and a target data object from one of the target image samples, and (ii) the corresponding source class label for the source data object and the corresponding target class label for the target data object. The method also includes adjusting the current set of configuration parameters to minimize a loss function for the machine learning model for the plurality of mixed data elements. The method results in adapted machine learning model that accounts for domain shift and that has improved performance at inference on new target image samples.