Deep Neural Network Camera Sensor Simulation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle camera sensor modeling systems struggle to accurately represent the imager's behavior in software simulations, particularly in characterizing defects like black pixels and noise, and tuning parameters such as contrast and white balance, which are complex and costly to model accurately.
Innovation Solution
A software-in-loop system using machine learning algorithms, specifically deep neural networks, to mimic the physical camera sensor's behavior by learning the mapping between input and output image data, allowing for the characterization of both the imager and lens in a software environment, with a Generative Adversarial Network (GAN) used to iteratively refine the simulation output to match the physical sensor's output.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional parameterized modeling methods are used to model camera sensors, then the modeling process is simpler and more straightforward, but the accuracy in representing imager behavior (defects, noise, tuning parameters) deteriorates
Solution Approach 1:
The patent creates a software copy of the physical camera sensor using deep neural networks that replicates the sensor's behavior including defects, noise characteristics, and parameter tuning. This virtual copy allows accurate representation of imager behavior without physical hardware while maintaining modeling simplicity through software-based approaches.
Solution Approach 2:
The patent replaces traditional mechanical/physical camera sensor modeling with a software-based deep learning system. The physical sensor is substituted by a neural network model that processes images through learned transformations, eliminating the need for complex physical parameter tuning while accurately capturing sensor behavior characteristics.
2Reliability
If physical camera sensors are used for testing and simulation, then accurate sensor behavior is obtained, but cost and complexity increase
Solution Approach 1:
The patent creates a virtual copy of the physical camera sensor that can be deployed in simulation environments. This software-based model replicates sensor behavior including optical characteristics, imager defects, and processing parameters, allowing comprehensive testing without requiring physical sensor hardware for each test scenario.
Solution Approach 2:
The trained deep neural network model serves multiple functions: it can simulate different camera sensors, represent various defect conditions, and model different tuning parameters all within a single software framework. This universal model replaces the need for multiple physical sensors and test setups.
3Measurement precision
If deep neural networks are used to model camera sensors, then accuracy in representing sensor behavior improves, but computational complexity and training requirements increase
Solution Approach 1:
The patent performs comprehensive training of the deep neural network model in advance using paired images from physical sensors and simulation environments. This preliminary training phase captures all sensor characteristics, defects, and parameter relationships, allowing the model to be deployed later without requiring complex computational resources during actual simulation or testing operations.
Data Source
AI summary
A camera simulation system or method or process for simulating performance of a camera for a vehicle includes providing a camera having a lens and imager and providing a learning algorithm. Image data is captured from a raw image input of the camera and the captured image data and raw image input are output from the camera. The output image data and the raw image input are provided to the learning algorithm. The learning algorithm is trained to simulate performance of the lens and/or the imager using the output captured image data and the raw image data input. The performance of the lens and/or the imager is simulated responsive to the learning algorithm receiving raw images.