Automatic Crop Row Camera Calibration via Homography
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Solution Overview
Problem
Existing crop row navigation systems relying on computer vision face challenges in accurately calibrating camera pitch and height, especially when used as aftermarket installations, leading to tedious and error-prone processes that can result in poor performance.
Innovation Solution
An automatic calibration method using image sets from multiple perspectives or a single image, where a mathematical model is fitted to the crop rows to determine calibration parameters such as pitch and height, allowing for precise translation of image units to navigational units through homography.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual calibration methods are used for camera pitch and height, then calibration can be performed, but the process becomes tedious and error-prone
Solution Approach 1:
The system performs automatic calibration by capturing images of crop rows and computing camera parameters (pitch and height) through image processing and mathematical modeling, eliminating the need for manual calibration operations. The calibration process serves itself by using the captured images to automatically determine the required parameters.
Solution Approach 2:
The patent replaces manual mechanical calibration procedures with an automated computer vision-based system. Instead of physically adjusting and measuring camera parameters manually, the system uses image processing, homography calculations, and mathematical optimization to automatically compute the calibration parameters.
2Reliability
If manual calibration procedures are followed, then calibration can be completed, but errors increase and performance deteriorates
Solution Approach 1:
The system performs preliminary calibration automatically by capturing images and computing parameters before navigation begins. This preliminary automated calibration ensures accurate camera parameter determination, improving navigation reliability while reducing the complexity of subsequent operations.
Solution Approach 2:
The system uses feedback from captured images to iteratively refine calibration parameters. By processing images and comparing results with expected geometric relationships, the system automatically adjusts and optimizes camera pitch and height values, improving reliability while maintaining manageable complexity.
3Measurement precision
If automatic calibration using image sets is implemented, then calibration accuracy improves, but processing complexity increases
Solution Approach 1:
The calibration process is segmented into distinct stages: image capture, feature detection (identifying crop row positions), homography calculation, and parameter optimization. This segmentation allows the complex task to be broken down into manageable steps, improving accuracy while controlling overall complexity.
Solution Approach 2:
The calibration system serves multiple functions: it captures images, detects crop row geometry, computes homography transformations, and determines camera parameters. This multi-functionality consolidates what would otherwise require separate systems into a unified calibration process, improving accuracy without proportionally increasing complexity.
Data Source
AI summary
System and techniques for calibrating a crop row computer vision system are described herein. An image set that includes crop rows and furrows is obtained. Models of the field are searched to find a model that best fits the field. A calibration parameter is extracted from the model and communicated to a receiver.