Agricultural Row Detection Using Wheel-Guided ROI Selection
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Solution Overview
Problem
Existing agricultural machines face challenges in accurately detecting crop rows or ridges for precise automatic steering, particularly when using image recognition techniques.
Innovation Solution
A row detection system employing multiple imaging devices mounted on an agricultural machine to capture images from different regions, including the positions of front and rear wheels, and a processor to convert these images into plan views for accurate row detection and steering control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single imaging device is used to capture ground surface images for row detection, then the device complexity is low, but the detection accuracy of crop rows or ridges is insufficient
Solution Approach 1:
The imaging system is segmented into multiple imaging devices (first imaging device for forward direction, second imaging device for backward direction) that capture different regions of the ground surface. Each device independently captures images of specific areas, and the processor integrates these multiple images to achieve comprehensive and accurate row detection, resolving the contradiction between detection accuracy and device complexity.
Solution Approach 2:
The system transitions from single-direction imaging to multi-directional imaging by adding a second imaging device that captures images in the backward direction. This dimensional expansion allows the system to detect row regions that may be missed by a single forward-facing device, improving detection accuracy while maintaining manageable system complexity through modular architecture.
2Measurement precision
If multiple imaging devices are used to improve row detection accuracy, then the detection accuracy of crop rows or ridges is improved, but the device complexity increases
Solution Approach 1:
The processor performs multiple functions: it processes images from both the first and second imaging devices, converts both images into plan view images, detects wheel positions, determines regions of interest, and performs row detection. This multi-functionality consolidates the complexity into a single processing unit, allowing multiple imaging devices to be used without proportionally increasing overall system complexity.
3Measurement precision
If the second imaging device captures images including wheel positions, then the region of interest can be accurately determined, but the image processing complexity increases
Solution Approach 1:
The processor performs preliminary actions by first converting both images into plan view images and detecting wheel positions before determining the region of interest. This preliminary processing of wheel position information allows the system to accurately define the region of interest in the first image, reducing the area that requires intensive row detection processing and thereby managing computational complexity.
Data Source
AI summary
A row detection system includes first and second imaging devices to be mounted on an agricultural machine, and a processor configured or programmed to perform image processing for first and second images which are generated by the first and second imaging devices, respectively. The second imaging device is located so that at least a portion of each front wheel and at least a portion of each rear wheel are included in the second image. The processor is configured or programmed to convert the first image into a first plan view image, convert the second image into a second plan view image, select a region of interest from the first plan view image based on a position of each front wheel and a position of each rear wheel in the second plan view image, and perform a row detection process targeted at the region of interest.