Farming Implement Pixel Masks for Model-Free Autonomous Vision
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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, fiducial markers, or accessed models to prevent the implement from being detected as an obstacle, allowing the machine to avoid collisions.
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 certain implements or modifications
Solution Approach 1:
The system performs self-service by automatically generating digital masks through image processing of sensor data without requiring external manufacturer models. The autonomous vehicle captures images of the implement, processes them through segmentation algorithms, and generates masks autonomously, eliminating dependency on external model availability.
Solution Approach 2:
The patent replaces the mechanical/manual process of creating CAD models and generating masks with an automated image processing system. Sensors capture visual data, computer vision algorithms segment the implement from the background, and digital masks are generated automatically, substituting manual modeling efforts with automated computational processes.
2Adaptability or versatility
If models are created from scratch using CAD software, then custom implements can be accommodated, but the process is time consuming and error prone
Solution Approach 1:
The system replaces time-consuming manual CAD modeling with automated image processing. Sensors capture images of the implement in its operational environment, computer vision algorithms automatically segment the implement features, and digital masks are generated computationally, reducing modeling time from hours or days to minutes or seconds.
Solution Approach 2:
Instead of creating models from scratch using CAD software, the system creates accurate digital representations by copying visual information directly from sensor images. The image processing algorithms capture the actual geometry and appearance of the implement as it exists, creating masks that reflect the true physical configuration without manual reconstruction errors.
3Adaptability or versatility
If manual modeling is performed, then custom implements can be represented, but accuracy is reduced due to errors
Solution Approach 1:
The system creates accurate digital masks by directly copying visual information from sensor images through automated image processing. The computer vision algorithms capture the actual geometry, contours, and features of the implement as they exist in reality, eliminating the manual interpretation and reconstruction errors that occur during traditional CAD modeling processes.
Solution Approach 2:
The patent replaces manual modeling operations with automated computational image processing. Sensors capture high-resolution images, algorithms automatically segment the implement from the background, and masks are generated through computational processes that maintain high precision without human error, achieving superior manufacturing precision compared to manual methods.
Data Source
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AI summary
ding 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.