Vehicle Camera System Adaptive Image Capture
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Solution Overview
Problem
Current vehicle camera systems face robustness issues in object detection, particularly in varying environmental conditions, as no single image processing technique effectively addresses the need for adaptive camera settings for enhanced object detection capabilities.
Innovation Solution
A camera system with a control module that captures images, compares them to a set of training images, and adjusts camera settings such as gain, exposure, and shutter speed to match those used in similar environmental conditions, optimizing object detection by dynamically configuring camera settings based on environmental conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If fixed camera settings are used, then device complexity is reduced, but object detection accuracy deteriorates in varying environmental conditions
Solution Approach 1:
The patent applies dynamics by making camera settings adjustable and adaptive rather than fixed. The control module dynamically modifies camera parameters (gain, exposure, shutter speed) based on real-time image analysis and environmental conditions, allowing the system to adapt to varying lighting, weather, and scene conditions while maintaining high object detection accuracy.
Solution Approach 2:
The patent implements parameter changes by modifying camera settings such as gain, exposure, and shutter speed based on analyzed environmental conditions. The control module compares captured images to training images and adjusts camera parameters accordingly, optimizing detection performance for different scenarios without requiring manual intervention.
2Measurement precision
If manual camera setting adjustment is used, then object detection accuracy can be optimized, but ease of operation deteriorates
Solution Approach 1:
The patent applies self-service by enabling the camera system to automatically adjust its own settings without manual intervention. The control module autonomously analyzes captured images, compares them to training data, and modifies camera parameters accordingly, making the system self-optimizing and eliminating the need for user expertise in camera configuration.
Solution Approach 2:
The patent implements feedback by using the captured images themselves to guide setting adjustments. The control module continuously monitors image quality and environmental conditions, compares results against training data, and modifies camera settings in real-time based on this feedback loop, ensuring optimal detection performance adapts automatically.
3Adaptability or versatility
If multiple camera settings are maintained for different conditions, then adaptability improves, but device complexity increases
Solution Approach 1:
The patent implements parameter changes by dynamically adjusting camera settings based on real-time environmental analysis. Rather than maintaining separate fixed configurations for different conditions, the system continuously adapts parameters (gain, exposure, shutter speed) based on image comparison with training data, achieving versatility through adaptive parameter modification rather than multiple static configurations.
Solution Approach 2:
The patent applies dynamics by transitioning from static camera configurations to dynamic, real-time adjustments. The control module continuously monitors environmental conditions and image quality, making on-the-fly modifications to camera settings, allowing the system to adapt to varying conditions without requiring pre-programmed configurations for each scenario.
Data Source
AI summary
A camera system for a vehicle. The system includes a camera configured to capture an image of an area about the vehicle, and a control module. The control module compares the captured image to a plurality of previously captured training images. The control module also determines which one of the plurality of training images is most similar to the captured image. Furthermore, the control module modifies settings of the camera to match camera settings used to capture the one or more of the plurality of training images that is most similar to the captured image.