Vehicular Guidance Using Crop Image Scan Lines
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Solution Overview
Problem
Existing vision systems for vehicular guidance using crop images are computationally demanding and prone to errors due to variations in crop rows or crop edges, making them unsuitable for real-time navigation.
Innovation Solution
A method and system that utilizes an imaging device to collect color image data, defines scan line segments perpendicular to the vehicle's axis, and employs an intensity evaluator and alignment detector to determine the vehicle's heading relative to crop features, reducing computational resources and improving accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional vision systems are used to infer relative position of vehicle with respect to crop image, then guidance accuracy may be maintained, but computational resources required 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 path. Instead of processing the entire image at once, the system processes individual scan lines independently, identifying crop row positions line-by-line. This segmentation dramatically reduces computational complexity and enables real-time processing while maintaining guidance accuracy.
Solution Approach 2:
The patent extracts only the essential information needed for guidance from the full image data. By focusing specifically on intensity variations along scan lines to detect crop row positions, the system eliminates unnecessary computational steps involved in processing complete image features, achieving faster response with reduced computational resources.
2Measurement precision
If traditional vision systems process complete image data to determine vehicle position, then comprehensive crop feature detection is achieved, but processing time increases making real-time navigation difficult
Solution Approach 1:
The patent segments the image into multiple scan lines and processes each line independently to detect crop row positions. This approach maintains detection precision by examining intensity variations across the entire field of view while reducing processing time through parallel, independent line-by-line analysis rather than comprehensive image processing.
Solution Approach 2:
The patent applies partial action by processing only the necessary scan lines and intensity data required for crop row detection, rather than analyzing all image features. This selective processing achieves sufficient measurement precision for guidance while minimizing processing time to enable real-time navigation.
3Reliability
If vision systems rely on crop row variations for guidance, then navigation information is obtained, but inaccuracies occur due to discontinuities or variations in the crop rows
Solution Approach 1:
The patent employs feedback mechanisms by continuously monitoring intensity variations along scan lines and adjusting vehicle positioning based on detected deviations from desired crop row alignment. This closed-loop approach compensates for crop variations and discontinuities, maintaining high guidance reliability and position inference accuracy despite imperfect crop conditions.
Solution Approach 2:
The patent changes the measurement parameter from relying on visual recognition of crop row features to measuring intensity variations along scan lines. This parameter transformation makes the system less sensitive to crop variations and discontinuities, improving both reliability and precision of position inference by focusing on quantitative intensity data rather than qualitative visual features.
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 intensity evaluator determines scan line intensity 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 intensity meeting or exceeding a maximum value or minimum threshold value.