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Storage, VM setup, and running the pipeline
BenchBot operators manually upload to blob container (“Upload”)
Blob container is mounted to VM
Cron job is scheduled to start pipeline every N hours
pipeline runs and stores data to temporary locations until complete
the last process is moving data to the appropriate blob container mounted on VM
processed batches are recorded
Pipeline
The pipeline includes seven main processes and places data in five blob containers.
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Infers global bounding box positions using autoSfM camera reference information.
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WHY?
Species mapping: Our The object detection model alone does not solve all our problems. It only detect plants, not species. Species information is necessary to create accurate and detailed label data.
Species mapping: Species level detection for this project (24 species) is unrealistic at this early stage. When a user-defined species map and geospatial data are applied, AutoSfM results can provide specie level information. If we know what row or general geographic area these species are located, then we can label each bounding box appropriately.
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Monitoring: Monitor for inconsistencies and error in image capture across sites using detailed reporting of camera reference information
Segment Vegetation and Cutout
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Data
After remapping bounding box coordinates
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Semi-Field Trial
Trial data was collected and used to develop the annotation pipeline and test camera settings. The trial was performed between early-March and late-April (2022) in NC under indoor conditions. Details about the data and results follow.
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