Object Detection Data Augmentation via Reinforcement Learning
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Solution Overview
Problem
Existing object detection models face challenges in data augmentation due to the high cost and inefficiency of manually labeling bounding shapes, limiting their performance and requiring extensive computational resources.
Innovation Solution
A computing system employing reinforcement learning to iteratively select augmentation operations, generating augmented training images that improve object detection model performance by leveraging a defined search space of operations, including those that modify or preserve bounding shapes, and updating the controller model based on performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual labeling of bounding shapes is used to collect training data, then object detection model performance can be improved, but the cost and time required increases significantly
Solution Approach 1:
The system performs preliminary data augmentation on existing labeled images before training the object detection model. By pre-processing the training data with various augmentation operations (rotation, scaling, cropping, color adjustments), the system creates additional diverse training examples without requiring manual labeling, thus improving model performance while saving time
Solution Approach 2:
The system creates copies of existing labeled training images and applies augmentation operations to generate variant versions. These copied and transformed images serve as additional training data, eliminating the need to manually label new images while providing sufficient training diversity for improved model performance
2Reliability
If more training data is collected to improve object detection performance, then model accuracy increases, but the cost of data collection and labeling increases
Solution Approach 1:
The system applies data augmentation operations as a preliminary step before model training, transforming existing training images into multiple augmented versions. This preliminary processing effectively increases the quantity of training data available without requiring proportional increases in manual labeling resources
Solution Approach 2:
The system generates multiple copies of each training image through augmentation operations, creating a expanded training dataset from a smaller original set. This copying approach maintains data diversity while reducing the need to collect and label large volumes of additional images
3Reliability
If extensive manual labeling is performed to obtain sufficient training data, then object detection performance improves, but computational resources and time are excessively consumed
Solution Approach 1:
The system performs data augmentation as a preliminary computational step that is more efficient than manual labeling. By automating the data preparation process through programmable augmentation operations, the system improves computational efficiency while still providing sufficient training data for high model performance
Solution Approach 2:
The system replaces the mechanical process of manual labeling with automated computational augmentation operations. This substitution eliminates human labor while using efficient algorithms to generate training data, significantly improving productivity and reducing the computational burden associated with extensive manual data collection
Data Source
AI summary
Example aspects of the present disclosure are directed to systems and methods for learning data augmentation strategies for improved object detection model performance. In particular, example aspects of the present disclosure are directed to iterative reinforcement learning approaches in which, at each of a plurality of iterations, a controller model selects a series of one or more augmentation operations to be applied to training images to generate augmented images. For example, the controller model can select the augmentation operations from a defined search space of available operations which can, for example, include operations that augment the training image without modification of the locations of a target object and corresponding bounding shape within the image and/or operations that do modify the locations of the target object and bounding shape within the training image.