Object Detection Domain Adaptation With Style Transfer Labels
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Solution Overview
Problem
Existing deep convolution neural network (DNN) architectures for object detection require extensive manually labeled datasets across different data domains, leading to inefficient model adaptations and resource-intensive domain transfers, particularly when deploying models from a source domain to a target domain with different environmental conditions.
Innovation Solution
An object detection method utilizing a plurality of deep convolution neural network layers that performs image style transfer and active domain adaptation, reducing the need for exhaustive labeled datasets in the target domain by converting annotated images from a source domain to a target domain style and generating annotations for unannotated images, thereby adapting the model to the target domain with a minority of labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a conservative model training approach is used to ensure reliable object detection across different data domains, then model accuracy is improved, but the requirement for exhaustively labeled datasets in the target domain increases significantly
Solution Approach 1:
The method performs style transfer on source domain images before training, converting them to match the target domain's visual characteristics. This preliminary transformation allows the model to learn domain-invariant features without requiring exhaustive labeling of the target domain, thereby maintaining reliability while reducing the quantity of labeled data needed.
Solution Approach 2:
The invention introduces a style transfer module as an intermediary between the source and target domains. This mediator transforms the visual appearance of source images to match the target domain style, enabling the model to generalize across domains without requiring extensive target domain labeled data, thus resolving the contradiction between reliability and data quantity requirements.
2Adaptability or versatility
If domain adaptation is performed to transfer model knowledge from source to target domain, then model adaptability is improved, but the time and resources required for training increase
Solution Approach 1:
The style transfer process is performed in advance during the training phase, transforming source domain images to match target domain characteristics before the main detection training. This preliminary action enables faster adaptation when deploying to new domains, as the model has already learned to handle domain variations, thereby improving adaptability while managing training time efficiently.
Solution Approach 2:
The method applies style transfer selectively to source domain images that are most beneficial for adapting to the target domain, rather than processing all possible images. This partial action approach maintains model adaptability while reducing the overall training time and computational resources required compared to exhaustive domain adaptation.
3Manufacturing precision
If extensive manually labeled datasets are used for training across different data domains, then manufacturing precision of the model is improved, but the ease of manufacture decreases due to resource intensity
Solution Approach 1:
The style transfer module serves as an intermediary that bridges the gap between source and target domains, allowing the model to achieve high training precision without requiring exhaustive manual labeling of the target domain. This intermediary transformation reduces the resource intensity of data preparation while maintaining manufacturing precision through domain-invariant feature learning.
Solution Approach 2:
The method creates transformed copies of source domain images that match the target domain's visual style. These copied and transformed images serve as synthetic training data, reducing the need for extensive manual labeling of actual target domain images while maintaining training precision through the preserved semantic content and transformed visual characteristics.
Data Source
AI summary
According to an exemplary embodiment, the disclosure provides an object detection method includes not limited to obtaining a set of a plurality of object annotated images in a source domain and have a first image style; obtaining a minority set of a plurality of object annotated images in a target domain and having a second image style; obtaining a majority set of a plurality of unannotated images which are in the target domain and having the second image style; performing an image style transfer to generate a converted set of object annotated images having the second image style; generating object annotation for the majority set of the plurality of unannotated images in the second image style to change from the majority set of a plurality of unannotated images into a majority set of a plurality of annotated images; and performing an active domain adaptation to generate an object detection model.