Single Camera Height Disparity Detection for Agricultural Navigation
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Solution Overview
Problem
Computer vision systems used in agricultural machines face challenges in determining the height disparity between features, such as crop rows and furrows, without the use of additional sensors, as existing methods require stereoscopic sensors or prior knowledge of object sizes, which may not be reliably available.
Innovation Solution
The system employs a single camera to differentiate between features by analyzing the spatial distribution of pixels, using variations in pixel intensity and color to determine height differences, allowing for the identification of crop rows and furrows based on their relative distances from the camera.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If stereoscopic sensors or multiple cameras are used to determine height differences, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The patent extracts the height difference information from the single camera image by analyzing pixel intensity variations and spatial distributions. Instead of using multiple cameras, the system extracts depth information from the single perspective image through computational analysis of pixel characteristics, thereby reducing device complexity while maintaining measurement capability.
Solution Approach 2:
The patent replaces the mechanical/stereoscopic sensor system with a computational vision approach. Instead of using physical multiple-camera setups to capture depth information, the system uses image processing algorithms to calculate height differences from single-camera images by analyzing pixel intensity and spatial distribution patterns.
2Measurement precision
If prior knowledge of object sizes is used to determine height differences, then measurement precision is improved, but reliability deteriorates
Solution Approach 1:
The system determines object heights autonomously by analyzing pixel intensity variations and spatial distributions in the images. Instead of relying on external prior knowledge of object sizes, the system self-determines height differences through computational analysis of the visual data, making the measurement reliable without requiring pre-stored dimensional information.
Solution Approach 2:
The patent changes the approach from using fixed prior knowledge parameters to dynamically analyzing pixel intensity parameters and spatial distribution characteristics. By measuring actual pixel characteristics in the image rather than relying on pre-stored object dimensions, the system adapts to varying conditions and maintains reliability across different agricultural environments.
Data Source
AI summary
A device to determine a height disparity between features of an image includes a memory including instructions and processing circuitry. The processing circuitry is configured by the instructions to obtain an image including a first repetitive feature and a second repetitive feature. The processing circuitry is further configured by the instructions to determine a distribution of pixels in a first area of the image, where the first area includes an occurrence of the repetitive features, and to determine a distribution of pixels in a second area of the image, where the second area includes another occurrence of the repetitive features. The processing circuitry is further configured by the instructions to evaluate the distribution of pixels in the first area and the distribution of pixels in the second area to determine a height difference between the first repetitive feature and the second repetitive feature.