Neural Auto-Exposure Control for HDR Object Detection
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Solution Overview
Problem
Existing image sensors struggle to capture a wide dynamic range of luminances in a single shot, leading to motion artifacts and increased production costs in HDR imaging, and conventional exposure control methods are limited in real-time applications like robotics and autonomous driving.
Innovation Solution
A neural auto-exposure network trained end-to-end with a downstream object detection task to predict optimal exposure values, using a synthetic image formation model and a hybrid neural network architecture for real-time exposure control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If HDR sensors use sequential capture methods to acquire multiple captures with different exposures, then the dynamic range coverage is improved, but motion artifacts occur and production cost increases
Solution Approach 1:
The patent applies preliminary action by pre-training a neural network model on synthetic HDR data before deploying it for real-time exposure prediction. The model is trained offline using computationally intensive synthetic data generation and preprocessing, so that during actual operation, the system can quickly predict optimal exposure values without performing complex real-time calculations or requiring multiple sequential captures. This preliminary preparation eliminates motion artifacts by enabling single-shot HDR capture.
Solution Approach 2:
The patent uses copying by generating synthetic HDR training data that replicates real-world HDR scenarios. Instead of requiring actual physical HDR captures for training, the system creates artificial HDR images through computational methods, then uses these copies to train the neural network. This allows the model to learn from extensive HDR data without the practical constraints of capturing real HDR sequences, thereby avoiding motion artifacts in the training process.
2Adaptability or versatility
If HDR sensors use sequential capture methods to acquire multiple captures with different exposures, then the dynamic range coverage is improved, but production cost increases
Solution Approach 1:
The patent uses copying by generating synthetic HDR training data that replicates real-world HDR scenarios. Instead of requiring actual physical HDR captures for training, the system creates artificial HDR images through computational methods, then uses these copies to train the neural network. This allows the model to learn from extensive HDR data without the practical constraints of capturing real HDR sequences, thereby avoiding motion artifacts in the training process.
Solution Approach 2:
The patent replaces the mechanical sequential capture system with a computational neural network-based single-shot capture system. Instead of physically capturing multiple images at different exposures using hardware mechanisms, the system uses a trained neural network to predict optimal exposure parameters for single-shot capture, substituting mechanical complexity with computational intelligence and reducing production costs.
3Device complexity
If conventional exposure control methods are used in real-time applications, then the system complexity is low, but the object detection performance in HDR environments is insufficient
Solution Approach 1:
The patent applies preliminary action by pre-training a neural network model on synthetic HDR data before deploying it for real-time exposure prediction. The model is trained offline using computationally intensive synthetic data generation and preprocessing, so that during actual operation, the system can quickly predict optimal exposure values without performing complex real-time calculations or requiring multiple sequential captures. This preliminary preparation eliminates motion artifacts by enabling single-shot HDR capture.
4Object-generated harmful factors
If single-shot capture is used with conventional sensors, then motion artifacts are avoided and production cost is reduced, but the dynamic range coverage is insufficient
Solution Approach 1:
The patent replaces the mechanical sequential capture system with a computational neural network-based single-shot capture system. Instead of physically capturing multiple images at different exposures using hardware mechanisms, the system uses a trained neural network to predict optimal exposure parameters for single-shot capture, substituting mechanical complexity with computational intelligence and reducing production costs.
Solution Approach 2:
The patent applies parameter changes by using the neural network to dynamically adjust exposure parameters (such as exposure time and gain) based on the input image characteristics. The model predicts optimal exposure values that adapt to different lighting conditions, enabling conventional sensors to achieve effective HDR performance through intelligent parameter adjustment rather than physical multi-capture mechanisms.
Data Source
AI summary
An auto-exposure control is proposed for high dynamic range images, along with a neural network for exposure selection that is trained jointly, end-to-end with an object detector and an image signal processing (ISP) pipeline. Corresponding method and system for high dynamic range object detection are also provided.