Autonomous Farming Implement Pixel Masks for Obstacle Detection
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Solution Overview
Problem
Existing systems for generating digital masks for farming implements in autonomous farming machines are not easily applicable for all types of implements, especially those with modifications, as they often rely on manufacturer models that may not be available, and manual modeling is time-consuming and prone to errors.
Innovation Solution
A control system generates a pixel mask for a farming implement by isolating its pixels from surrounding environment pixels, using image segmentation, projection, depth measurements, or fiducial markers to create a mask that prevents the implement from being detected as an obstacle, allowing the machine to perform autonomous farming actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If manufacturer models are used to generate digital masks, then mask generation is simplified, but availability is limited for all farming implements
Solution Approach 1:
The system creates digital copies (pixel masks) of farming implements by capturing images with sensors and processing them through image segmentation. This copying approach allows masks to be generated for any implement that can be imaged, eliminating the need for manufacturer-provided models and enabling universal applicability across all implement types and modifications.
Solution Approach 2:
The patent replaces manual CAD modeling with automated image processing and machine learning algorithms. The sensor-based image capture and automated segmentation replace time-consuming manual modeling, making mask generation accessible for all implements without requiring expert intervention or manufacturer cooperation.
2Adaptability or versatility
If manual modeling is used to create digital masks, then custom implements can be accommodated, but the process is time-consuming and error-prone
Solution Approach 1:
The system performs self-service by automatically capturing images of the farming implement with onboard sensors and autonomously generating pixel masks through image processing algorithms. This eliminates the need for external manual modeling, reducing generation time from hours or days to minutes while maintaining accuracy for custom implements.
Solution Approach 2:
The patent introduces an intermediary image processing system that acts as a mediator between the physical implement and the autonomous vehicle's navigation system. This intermediary automatically converts visual data into pixel masks, eliminating time-consuming manual intervention while ensuring accurate representation of custom implements.
3Manufacturing precision
If manual modeling is used to create digital masks, then implement-specific accuracy can be achieved, but the process is prone to errors
Solution Approach 1:
The system incorporates feedback mechanisms where the generated pixel masks are validated against the original sensor images and real-time obstacle detection data. This feedback loop identifies and corrects errors in mask generation, ensuring high accuracy while reducing the error rate through automated verification and adjustment.
Solution Approach 2:
The patent replaces error-prone manual modeling with automated machine learning-based image segmentation. This substitution eliminates human errors in mask creation while maintaining high precision through algorithmic consistency and automated validation against sensor data, significantly improving reliability.
Data Source
AI summary
A control system accesses a first set of images corresponding to the farming implement of the autonomous vehicle. Each image in the first set of images comprises pixels of the farming implement and a surrounding environment. The control system generates a pixel mask for the farming implement based on the first set of images, the pixel mask configured to ignore pixels of the farming implement in images to which the pixel mask is applied. The control system accesses a second set of images corresponding to the farming implement and surrounding environment. The control system generates a masked set of images from the second set of images by applying the pixel mask to the second set of images to ignore the pixels of the farming implement in the second set of images. The control system performs a farming action based on the masked set of images, using the autonomous vehicle.