Autonomous Vehicle Perception Images With Minimal Color Preprocessing
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Solution Overview
Problem
Autonomous vehicle image processing requires significant data complexity and bandwidth for analysis, which is inefficient and not optimized for machine vision tasks, as existing methods include unnecessary steps like de-mosaicing and white balancing that do not add value for machine vision.
Innovation Solution
The method reduces data complexity by selecting one color value per pixel using a pattern like the Bayer pattern, reducing data size to 1/3 of full color RGB, and minimizing preprocessing to enhance processing speed and reduce computational requirements, while maintaining color sensitivity for object detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If full color RGB image processing is used, then color information is complete, but data size and computational requirements increase significantly
Solution Approach 1:
The patent extracts only the necessary color information for machine vision tasks by selecting one color value per pixel using a pattern (e.g., Bayer pattern), discarding redundant color data. This extraction approach maintains sufficient color sensitivity for object detection while reducing data size to 1/3 of full RGB, directly resolving the contradiction between information completeness and processing complexity.
2Measurement precision
If traditional preprocessing steps (de-mosaicing, white balancing) are applied, then image quality for human perception is improved, but processing time and computational load increase
Solution Approach 1:
The patent removes unnecessary preprocessing steps (de-mosaicing, white balancing) that are optimized for human perception but add computational overhead for machine vision. By extracting only the essential processing needed for machine analysis, the system achieves real-time processing speeds while maintaining sufficient image quality for object detection.
Solution Approach 2:
Instead of applying traditional image processing pipelines designed for human viewing, the patent inverts the approach by directly processing the raw sensor output with minimal preprocessing tailored specifically for machine vision algorithms. This inversion eliminates redundant steps and optimizes the processing chain for computational efficiency.
3Loss of information
If full RGB data is transmitted and stored, then complete color information is preserved, but bandwidth and storage requirements increase
Solution Approach 1:
The patent extracts only the essential color information needed for autonomous vehicle perception by selecting one color value per pixel in a patterned approach. This extraction reduces data volume to 1/3 of full RGB while preserving sufficient color sensitivity for detecting objects and their properties, directly addressing the contradiction between information completeness and data quantity.
Data Source
AI summary
Disclosed are devices, systems and methods for processing an image. In one aspect a method includes receiving an image from a sensor array including an x-y array of pixels, each pixel in the x-y array of pixels having a value selected from one of three primary colors, based on a corresponding x-y value in a mask pattern. The method may further include generating a preprocessed image by performing preprocessing on the image. The method may further include performing perception on the preprocessed image to determine one or more outlines of physical objects.