Adversarial Object Modeling for Robust UAV-Based Recognition
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Solution Overview
Problem
Existing object recognition algorithms perform suboptimally under non-uniform lighting and dynamic conditions, leading to inaccuracies in object recognition.
Innovation Solution
Employing adversarial learning through generative adversarial networks (GANs) to refine object models by generating adversarial examples, which are used to improve the accuracy of object models by capturing additional measurements when predictions fail, and updating the model to include these measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If object recognition algorithms are trained under uniform lighting and static conditions, then recognition accuracy is improved under those specific conditions, but recognition performance deteriorates in varying environments with different lighting and angles
Solution Approach 1:
The system performs preliminary actions by capturing measurements of physical properties (reflectivity, roughness, transparency) before recognition tasks. These measurements are used to pre-train the object model with diverse environmental characteristics, enabling the algorithm to adapt to varying lighting and viewing conditions without requiring real-time retraining.
Solution Approach 2:
The invention changes the parameters used for object modeling by incorporating physical property measurements (reflectivity, roughness, transparency) alongside traditional visual features. This multi-parameter approach allows the object model to represent objects under different lighting and viewing conditions, improving recognition accuracy across varying environments.
2Measurement precision
If the object model is updated with additional measurements from areas where predictions fail, then measurement precision is improved, but the complexity of the modeling process increases
Solution Approach 1:
The system implements a feedback mechanism where prediction failures are detected and trigger targeted additional measurements. When the object model fails to accurately predict physical properties in certain regions, the system automatically captures additional measurements from those specific areas and updates the model, creating a closed-loop improvement process that focuses computational resources only where needed.
3Reliability
If adversarial examples are generated and used to train the object model, then robustness against misleading inputs is improved, but the computational resources and time required for training increase
Solution Approach 1:
The system applies partial adversarial training by generating and using only the most critical adversarial examples that target the object model's weakest points. Rather than exhaustively training on all possible adversarial variations, the method focuses computational effort on representative adversarial cases, achieving improved robustness with reduced training time and resources.
Data Source
AI summary
Methods, computer-readable media, and devices are disclosed for improving an object model based upon measurements of physical properties of an object via an unmanned vehicle using adversarial examples. For example, a method may include a processing system capturing measurements of physical properties of an object via at least one unmanned vehicle, updating an object model for the object to include the measurements of the physical properties of the object, where the object model is associated with a feature space, and generating an example from the feature space, where the example comprises an adversarial example. The processing system may further apply the object model to the example to generate a prediction, capture additional measurements of the physical properties of the object via the at least one unmanned vehicle when the prediction fails to identify that the example is an adversarial example, and update the object model to include the additional measurements.