Vehicular Guidance Using Crop Image Scan Lines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing vision systems for vehicular guidance using crop images are computationally demanding and often inaccurate due to variations in crop rows or crop edges, making them unsuitable for real-time navigation.
Innovation Solution
A method and system that utilize an imaging device to collect color image data, define scan line segments perpendicular to the vehicle's axis, and use an image parameter evaluator and alignment detector to determine the vehicle's heading aligned with crop features, while a reliability estimator ensures the accuracy of the guidance data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional vision systems process crop images to determine vehicle position, then guidance information can be obtained, but computational resources are excessive and response time is too slow for real-time navigation
Solution Approach 1:
The patent divides the image processing task into segmented scan lines perpendicular to the vehicle's transverse axis. Instead of processing the entire image at once, the system evaluates image parameters along individual scan lines, significantly reducing computational complexity while maintaining positioning accuracy. This segmentation allows real-time processing by breaking down the complex image analysis into manageable, sequential operations.
Solution Approach 2:
The system extracts only the essential image parameters needed for vehicle positioning by evaluating specific scan line segments rather than processing all image data. The alignment detector extracts heading information by comparing scan line image parameters against reference values, discarding redundant background information while retaining critical guidance data.
2Measurement precision
If vision systems process variations in crop rows or crop edges to determine vehicle position, then comprehensive guidance data can be obtained, but measurement accuracy decreases due to discontinuities and variations in the crop
Solution Approach 1:
The system uses partial action by evaluating image parameters only along specific scan line segments rather than analyzing the entire image. This selective approach focuses computational effort on critical regions while ignoring areas with crop variations or discontinuities. The alignment detector determines vehicle heading by analyzing only the necessary scan lines, reducing the impact of crop variations on measurement accuracy.
Solution Approach 2:
The patent changes the approach from analyzing crop feature variations to evaluating image parameters (such as brightness, contrast, or color) along scan lines. By transforming the measurement parameter from crop morphology to optical properties, the system becomes less sensitive to crop row variations and discontinuities, improving reliability under varying crop conditions.
3Loss of information
If the system processes all image data including background to determine vehicle heading, then complete scene understanding is achieved, but computational demand increases and response time decreases
Solution Approach 1:
The system extracts only the essential information needed for vehicle heading determination by processing specific scan line segments rather than analyzing the complete image. The alignment detector extracts heading information from scan line image parameters, discarding unnecessary background data while retaining critical navigation information. This selective extraction reduces processing time while maintaining sufficient scene understanding for guidance.
Solution Approach 2:
The patent segments the image processing task into discrete scan line evaluations, allowing the system to process only relevant portions of the scene. By dividing the image into horizontal scan lines and evaluating parameters only along these segments, the system reduces computational demand while preserving essential spatial information needed for accurate vehicle heading determination.
Data Source
AI summary
The method and system for vehicular guidance comprises an imaging device for collecting color image data to facilitate distinguishing crop image data (e.g., crop rows) from background data. A definer defines a series of scan line segments generally perpendicular to a transverse axis of the vehicle or of the imaging device. An image parameter evaluator determines scan line image parameter data for each of the scan line segments. An alignment detector (e.g., search engine) identifies a preferential heading of the vehicle that is generally aligned with respect to a crop feature, associated with the crop image data, based on the determined scan line image parameter meeting or exceeding a maximum value or minimum threshold value. A reliability estimator estimates a reliability of the vehicle heading based on compliance with a criteria for scan line image parameter data associated with one or more crop rows.