Farming Implement Pixel Mask Validation for Obstacle Detection

Solution Overview

Problem

Existing digital masks for farming implements in autonomous farming machines often fail to accurately represent the shape of the implements, leading to potential collisions or missed field edges, as they may be too small or large, thereby affecting the machine's ability to navigate and perform farming actions effectively.

Innovation Solution

A control system validates pixel masks for farming implements by generating and applying them to images, determining their validity based on threshold pixel inclusion and environmental representation, ensuring accurate isolation of implement pixels from surrounding environment pixels.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If a digital mask is made smaller to reduce false obstacle detection, then the farming implement can operate closer to detected objects, but the mask may become too small and cause the implement to collide with actual obstacles

Engineering Contradiction:

Improveobstacle detection accuracyVSAvoidcollision risk

Core Design Contradiction:

ReliabilityVSObject-affected harmful factors

Solution Approach 1:

The system performs preliminary validation of the digital mask by comparing it against actual sensor images of the farming implement. Before operational use, the mask is tested to ensure it accurately represents the implement's shape and dimensions, preventing both false detections and collision risks in subsequent operations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses sensor images captured during operation to provide feedback on mask accuracy. The digital mask is continuously validated against actual visual data of the farming implement, allowing the system to detect and correct mask inaccuracies that could lead to either false obstacle detection or collision risks.

Inventive Principle:

Principle #23Feedback

2Object-affected harmful factors

If a digital mask is made larger to ensure complete coverage of the farming implement, then collision risk is reduced, but the mask prevents the implement from getting as close to obstacles as it normally would

Engineering Contradiction:

Improvecollision riskVSAvoidfield coverage efficiency

Core Design Contradiction:

Object-affected harmful factorsVSProductivity

Solution Approach 1:

The system performs preliminary validation of the digital mask by comparing it against actual sensor images of the farming implement. Before operational use, the mask is tested to ensure it accurately represents the implement's shape and dimensions, preventing both false obstacle detection and collision risks in subsequent operations.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system uses sensor images captured during operation to provide feedback on mask accuracy. The digital mask is continuously validated against actual visual data of the farming implement, allowing the system to detect and correct mask inaccuracies that could lead to either false obstacle detection or collision risks.

Inventive Principle:

Principle #23Feedback

3Ease of manufacture

If a digital mask is generated from manufacturer models or CAD software without validation, then the process is simpler and faster, but the mask may not accurately capture the actual shape of the farming implement

Engineering Contradiction:

Improvemask generation simplicityVSAvoidmask shape accuracy

Core Design Contradiction:

Ease of manufactureVSMeasurement precision

Solution Approach 1:

Instead of relying solely on manufacturer models or CAD software, the system creates an actual visual copy of the farming implement using sensor images captured in the real operating environment. This empirical copy accurately reflects the implement's true shape, including any modifications or wear, providing a more precise basis for the digital mask.

Inventive Principle:

Principle #26Copying

Solution Approach 2:

The system uses sensor images captured during operation to provide feedback on mask accuracy. The digital mask is continuously validated against actual visual data of the farming implement, allowing the system to detect and correct mask inaccuracies that could lead to either false obstacle detection or collision risks.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS20260038269A1Validation of masks for farming implements of autonomous vehicles

Publication Date: 2026.02.05 DEERE & CO

AI summary

A method of validating a pixel mask for a farming implement of an autonomous farming machine. A system may access a set of images corresponding to the farming implement, each image in the set of images comprising pixels of the farming implement and a surrounding environment. The system may generate a masked set of images from the set of images by applying the pixel mask to the set of images to ignore the pixels of the farming implement from the set of images. The system may determine, based on the masked set of images, the pixel mask is a valid pixel mask or an invalid pixel mask. Responsive to determining the pixel mask is a valid pixel mask, the system performs a first farming action. Responsive to determining the pixel mask is an invalid pixel mask, the system performs a second farming action different from the first farming action.