Vehicle Camera Image Subsection Processing for Driver Assistance
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current vehicle camera image analysis methods require processing large images to detect small objects, leading to high computational loads and power consumption in driver assistance systems, especially at night or in low light conditions.
Innovation Solution
The method involves analyzing and transmitting only specific subsections of the image containing recognized objects to relevant driver assistance modules, reducing the data processing load by pre-processing the image to identify objects and their parameters, such as position, color, and brightness, and then using these subsections for analysis instead of the entire image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the entire image is processed by driver assistance modules to detect small objects, then detection completeness is improved, but computational load and power consumption increase significantly
Solution Approach 1:
The image is divided into multiple subregions, with each subregion assigned to a specific driver assistance module for processing. This segmentation allows parallel processing of different image portions, reducing the computational burden on individual modules while maintaining comprehensive object detection across the entire image.
Solution Approach 2:
Each driver assistance module processes only a partial portion of the image (its assigned subregion) rather than the entire image. This partial action approach reduces computational load and power consumption while still achieving complete coverage when all modules' results are combined.
2Measurement precision
If the entire image is analyzed by driver assistance modules, then object detection accuracy is improved, but processing time increases
Solution Approach 1:
The image processing task is segmented into multiple parallel operations, with each driver assistance module analyzing its assigned subregion simultaneously. This parallel processing approach maintains detection accuracy while significantly reducing overall processing time compared to sequential analysis of the entire image.
Solution Approach 2:
Each module performs partial analysis on its designated subregion rather than analyzing the complete image. This partial action enables faster local processing while the aggregation of results from multiple modules achieves comprehensive detection accuracy.
3Reliability
If all driver assistance modules process the complete image, then system reliability is improved, but device complexity increases
Solution Approach 1:
The system architecture is segmented such that each driver assistance module handles a specific subregion of the image. This segmentation reduces the data processing complexity for each individual module while maintaining system reliability through the combined coverage of all modules.
Solution Approach 2:
The patent extracts and processes only the relevant subregion of the image for each driver assistance module's specific function. This extraction approach reduces unnecessary data processing and system complexity while maintaining the reliability needed for each module's specialized detection task.
Data Source
AI summary
A method for analyzing an image recorded by a camera of a vehicle. The method includes a step of reading the image of the camera. Furthermore, the method includes a step of recognizing at least one object in a subsection of the image, the subsection imaging a smaller area of the vehicle surroundings than the image. Furthermore, the method includes a step of transmitting the subsection and/or information about the subsection of the image to a driver assistance module. Finally, the method includes a step of using the subsection of the image and/or the information about the subsection of the image instead of the entire image in the driver assistance module, in order to analyze the image recorded by the camera.