Farming Machine Vision Compensation for Camera Occlusions
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Solution Overview
Problem
Farming machines face challenges in maintaining detection mechanisms due to harsh operating environments, leading to occlusions that compromise their functionality, particularly in autonomous or semi-autonomous systems where high-quality imaging is required.
Innovation Solution
The farming machine identifies occlusions through image analysis, adjusts capture parameters to compensate for them, and generates dynamic masks to remove obstructed pixels or classifier outputs, ensuring continued operation and accurate treatment execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the farming machine operates in harsh environments, then productivity is maintained, but occlusions occur that compromise detection mechanism functionality
Solution Approach 1:
The system automatically detects occlusions using image analysis and static object detection, generates dynamic masks to compensate for occluded regions, and adjusts capture parameters without requiring manual intervention from the manager, enabling the farming machine to self-correct and maintain continuous operation
Solution Approach 2:
The system employs dynamic masks that are generated and updated in real-time based on detected occlusions, allowing the detection system to adaptively compensate for changing occlusion conditions rather than using fixed compensation methods
2Reliability
If the manager manually clears occlusions, then detection mechanism functionality is restored, but productivity is reduced due to operational interruptions
Solution Approach 1:
The system automatically detects occlusions using image analysis and static object detection, generates dynamic masks to compensate for occluded regions, and adjusts capture parameters without requiring manual intervention from the manager, enabling the farming machine to self-correct and maintain continuous operation
3Measurement precision
If dynamic masks are generated to remove occluded pixels, then measurement precision is maintained, but device complexity increases
Solution Approach 1:
The system performs static object detection and generates dynamic masks in advance before treating plants, preparing compensated images that remove occlusion effects beforehand, which simplifies the subsequent plant detection process rather than adding complexity during treatment execution
Solution Approach 2:
The dynamic mask acts as an intermediary layer between the occluded image and the plant detection algorithm, selectively removing or masking occluded regions to provide clean input data to the detection system without requiring complex modifications to the detection algorithm itself
Data Source
AI summary
A farming machine is configured to identify and compensate for occlusions in the field of view of its image acquisition system. To do so, the machine captures an image using a first set of capture parameters associated with a first set of treatment results. The farming machine identifies an occlusion in the first image that obstructs a portion of the first image and determines occlusion characteristics representative of the occlusion based on image data in the first image. The farming machine compensates for the identified occlusion based on the occlusion characteristics. The farming machine captures a second image using modified set of capture parameters that compensate for the occlusion. The second image is associated with a second set of treatment results. The farming machine transmits the second set of treatment results to a manager of the farming machine.