Targeted Image Preprocessing for Task-Specific Feature Detection
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Solution Overview
Problem
Existing image analysis systems for driver assistance and autonomous driving often degrade the information required for analysis due to unsuitable preprocessing, which can lead to suboptimal results and inefficient use of computational resources.
Innovation Solution
A modular system with a self-learning preprocessing module that generates intermediate images tailored to specific analysis needs, allowing for independent optimization of preprocessing and analysis, and utilizing a neural network architecture for efficient parallel processing and resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If traditional rigid preprocessing (rectification and color processing) is applied to input images, then the images are standardized for processing, but relevant information for analysis is lost or degraded
Solution Approach 1:
The patent implements dynamic preprocessing where the processing chain adapts its parameters based on the specific analysis task. Instead of fixed rectification and color processing, the system dynamically adjusts preprocessing operations to preserve task-relevant information while maintaining standardization benefits.
Solution Approach 2:
The system changes preprocessing parameters based on the analysis requirements. Different tasks (e.g., object detection, lane detection) receive customized preprocessing with adjusted parameters to preserve the specific information needed for each task while still providing standardized output formats.
2Device complexity
If a unified preprocessing approach is used for all analysis tasks, then system complexity is reduced, but the information required for specific analyses may be suboptimally preserved
Solution Approach 1:
The patent segments the preprocessing system into multiple specialized processing chains, each optimized for specific analysis tasks. This segmentation allows each chain to preserve task-relevant information while the overall system remains manageable through modular architecture and shared components.
Solution Approach 2:
The system achieves universality through a parameterized preprocessing framework that can be configured for different tasks using the same underlying infrastructure. Multiple processing chains share common components but are customized through parameters to serve different analysis requirements, balancing specialization with system simplicity.
3Reliability
If preprocessing is tightly coupled with analysis in a single K1 module, then learning can be jointly optimized, but replacing or updating either component causes loss of learning progress
Solution Approach 1:
The patent segments the previously unified K1 module into separate preprocessing and analysis modules with well-defined interfaces. This segmentation enables independent replacement and updating of each module while preserving learning progress through interface standardization and modular architecture.
Solution Approach 2:
The system implements feedback mechanisms where analysis results inform preprocessing parameter adjustments. This feedback loop maintains joint optimization benefits while allowing independent module updates, as the feedback ensures continuous adaptation to task requirements regardless of which module is replaced.
4Measurement precision
If full information from input images is preserved through preprocessing, then analysis accuracy improves, but computational resources and power consumption increase
Solution Approach 1:
The patent applies local quality by preserving full information only in regions and at stages where it is most needed for specific analysis tasks. The parameterized preprocessing chains selectively maintain or compress information based on task requirements, reducing overall computational load while preserving accuracy-critical information.
Solution Approach 2:
The system applies partial action by selectively preserving information rather than maintaining full image data throughout all processing stages. Each preprocessing chain preserves only the information excess needed for its specific task, reducing computational resources and power consumption while maintaining sufficient accuracy for the intended analysis.
Data Source
AI summary
A system includes a K1 preprocessing module designed to generate at least one intermediate image from an input image using a parameterized internal processing chain and an analysis module to detect a feature or object in the intermediate image. A method to train the system includes feeding a plurality of learning input images to the system, comparing a result provided by the analysis module for each of the learning input images to a learning value, and feeding back a deviation obtained by the comparison to an input preprocessing module and/or adapting parameters of the internal processing chain to reduce the deviation.