Preprocessing Algorithm for Image Classification Domain Adaptation
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Solution Overview
Problem
Pre-trained classification algorithms often produce poor results when faced with input data that differs significantly from their training data in terms of quality, resolution, or capture hardware/software, such as low-quality images from UAVs or gliders, due to the lack of corresponding high-quality data for training.
Innovation Solution
A preprocessing algorithm is trained to transform low-quality image data into a format compatible with pre-trained classification algorithms, using techniques like CycleGAN to preserve content while adjusting style and resolution, allowing better classification without requiring high-quality training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a pre-trained classification algorithm is used with high-quality training data, then classification accuracy is improved, but it produces poor results when applied to low-quality data from different domains
Solution Approach 1:
A preprocessing algorithm acts as an intermediary between low-quality input data and the pre-trained classification algorithm. This preprocessing step transforms the low-quality data into a format that is compatible with the pre-trained model's expectations, enabling the model to achieve high classification accuracy on data domains it was not originally trained on, without modifying the pre-trained model itself
2Device complexity
If pre-trained classification algorithms are applied directly to low-quality data, then device complexity is reduced, but classification accuracy deteriorates
Solution Approach 1:
A preprocessing step is performed before the main classification task to prepare the low-quality data. This preliminary action transforms the input data into a format suitable for the pre-trained classification algorithm, ensuring high classification accuracy while maintaining relatively simple system architecture by reusing existing pre-trained models
Data Source
AI summary
The present disclosure is directed to data classification. An exemplary computer-enabled method for classifying image data comprises: receiving an input image, wherein the input image is of a second data domain; providing the input image to a preprocessing algorithm to obtain a transformed image, wherein the preprocessing algorithm is trained to transform data of the second data domain to data of a first data domain; providing the transformed image to a trained algorithm to analyze the transformed image, wherein the trained algorithm is trained based on training data of the first data domain.