Neural Network Retraining for Camera Soiling ODD Detection
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Solution Overview
Problem
Machine learning systems, such as convolutional neural networks, struggle to accurately detect objects in environments with varying conditions that deviate from their operational design domain (ODD), particularly due to camera soiling, which reduces their ability to identify objects with high confidence.
Innovation Solution
Retraining the machine learning system using a generated training dataset that includes images modified by Perlin noise to simulate camera lens soiling, allowing it to output a confidence value indicating whether an image is within the ODD, and using gradient descent to determine optimal Perlin noise parameters for retraining.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the machine learning system is trained on standard images without soiling simulation, then the training process is simple and fast, but the system cannot accurately detect objects when camera lens is soiled
Solution Approach 1:
The patent applies preliminary action by pre-generating training images with simulated camera soiling using Perlin noise before the actual training process. This allows the machine learning system to learn from diverse soiling conditions in advance, improving its reliability when encountering real soiling scenarios without adding complexity to the operational detection process.
Solution Approach 2:
The patent utilizes parameter changes by varying Perlin noise parameters (amplitude, frequency, seed values) to generate diverse soiling patterns in the training dataset. This approach creates multiple realistic soiling scenarios from a single base image set, enhancing the system's ability to handle various soiling conditions while maintaining manageable dataset generation complexity.
2Reliability
If Perlin noise is added to simulate camera soiling in training images, then the system can detect objects better under soiling conditions, but the training dataset generation becomes more complex
Solution Approach 1:
The patent applies copying by generating synthetic soiling patterns through Perlin noise and overlaying them on clean training images. This creates realistic soiled versions of existing images without requiring physical soiling of actual camera lenses, thereby improving detection reliability while avoiding the logistical complexity of capturing real soiling scenarios.
Solution Approach 2:
The patent uses parameter changes in Perlin noise generation (controlling noise amplitude, scale, and randomness) to create varied soiling effects. By adjusting these parameters, the system generates a comprehensive training dataset covering different soiling severities and patterns, enhancing detection robustness while maintaining controlled complexity through algorithmic generation.
3Adaptability or versatility
If the machine learning system processes more diverse training images with various soiling conditions, then it can operate reliably outside its operational design domain, but the training time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-processing and augmenting training images with Perlin noise-based soiling simulations before training begins. This preparation work creates a comprehensive dataset that covers edge cases and environmental variations, allowing the system to be trained once on diverse conditions rather than requiring continuous retraining, thus reducing overall training time while improving adaptability.
Data Source
AI summary
A computer that includes a processor and a memory, the memory including instructions executable by the processor to generate a training dataset that includes images that are outside an operational design domain of a machine learning system by modifying the images by adding Perlin noise, wherein the machine learning system is trained to detect an object in an acquired image. The neural network can be retrained to output a confidence value greater than a threshold when an image is determined to be inside the operational design domain based on the generated training dataset.