Camera Performance Threshold Detection via Gradient Image Analysis

The patent applies preliminary action by pre-processing captured images through multiple transformations (rotations, flips, color space conversions, noise additions) before they are fed to the deep learning model. This creates a comprehensive test dataset that proactively covers various edge cases and failure scenarios, allowing the system to determine performance thresholds without requiring extensive real-world road testing. The gradient application to extracted objects further systematically explores performance boundaries by gradually modifying image characteristics.

The patent employs copying by creating multiple transformed versions of a limited set of captured images through gradient applications, rotations, flips, and color space conversions. Instead of requiring millions of miles of road testing to gather diverse scenarios, the system generates synthetic variations of existing images that simulate different edge cases and failure conditions. This approach efficiently produces large quantities of training data from minimal original captures, dramatically reducing the time and resources needed while maintaining comprehensive scenario coverage.

The patent applies segmentation by dividing the complex testing process into distinct modular stages: initial image capture, object extraction, gradient application, transformed image generation, and performance threshold determination. Each stage handles a specific aspect of the process independently, making the overall complex task manageable and systematic. The gradient application itself is segmented into multiple incremental levels, allowing precise control over transformation intensity and systematic exploration of performance boundaries without overwhelming complexity.