Multi-Camera Image Processing for Autonomous Agricultural Machines
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current autonomous agricultural work machines are costly due to the need for multiple computing units for each camera, increasing manufacturing costs and installation space requirements.
Innovation Solution
Implementing a single computing unit to evaluate images from multiple cameras, allowing for cost-effective image processing and display, with the option to adjust machine components and use predefined functions or artificial intelligence for obstacle detection and field navigation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If multiple computing units are used for each camera to enable autonomous operation and image display, then the machine's autonomous capability and image processing quality are improved, but the manufacturing cost and device complexity increase significantly
Solution Approach 1:
The patent merges the computing functions for multiple cameras into a single shared computing unit. Instead of having separate computing units for each camera, one computing unit processes images from all cameras, reducing the number of computing components while maintaining autonomous operation capability. This combining approach directly reduces device complexity and manufacturing cost.
Solution Approach 2:
The single computing unit is designed to perform multiple functions: it processes images from multiple different cameras, enables autonomous navigation, displays images on monitors, and supports various operating modes. This multi-functional design allows one computing unit to replace what would traditionally require multiple specialized computing units.
2Measurement precision
If multiple computing units are used for each camera, then the image processing capability is improved, but the manufacturing cost increases
Solution Approach 1:
The computing functions are merged into a single unit that handles all camera inputs. This consolidation reduces the total number of computing components needed, directly lowering manufacturing costs while maintaining the ability to process images from multiple cameras simultaneously or sequentially.
Solution Approach 2:
The single computing unit is designed with universal processing capabilities that can handle images from any of the multiple cameras. It performs obstacle detection, navigation processing, and image display functions regardless of which camera is capturing the image, providing cost-effective multi-functional processing.
3Measurement precision
If multiple computing units are used for each camera, then the image evaluation quality is improved, but the installation space requirements increase
Solution Approach 1:
By combining the computing functions into a single unit rather than having separate units for each camera, the physical space required for housing these computing components is significantly reduced. The single computing unit can be installed in one location within the vehicle, reducing the overall installation footprint.
4Ease of manufacture
If a single computing unit is used for all cameras, then the manufacturing cost is reduced, but the image processing load on the single unit increases
Solution Approach 1:
The computing unit processes images from multiple cameras in a segmented manner, handling one camera's image data at a time or in sequential batches rather than attempting to process all camera inputs simultaneously. This temporal segmentation reduces the peak processing load on the single computing unit while maintaining the ability to evaluate all camera feeds.
Data Source
Figure 1
Figure 2
AI summary
The present invention relates to an agricultural machine (1) for use in an agricultural field (2) with several cameras (3, 4, 5, 6, 7, 8, 9) for generating multiple images of several surrounding areas of the agricultural machine (1). The present invention is based on the general idea that only one processing unit (13) evaluates the images transmitted by the cameras (3, 4, 5, 6, 7, 8, 9).