: To save time, researchers used the virtual environment to automatically generate bounding boxes around objects, ensuring high precision for the AI training. Key Findings from the Research
: Recognizes if a worker is facing away or kneeling, which increases risk. 999 Part 1(1).mp4
: The study noted that moving machine parts (like an excavator's arm) can sometimes obstruct the view or cause perspective distortion, leading to slight distance errors. : To save time, researchers used the virtual
: The system significantly decreased the number of "nuisance" alarms compared to static sensors, as it understands when a worker or another machine is approaching safely for collaboration. : The system significantly decreased the number of
: The video frames were used to train YOLOv7 (You Only Look Once) and Mask-RCNN models to detect objects and estimate distances accurately in real-time.
: Distinguishes between workers, excavators, and forklifts.
Financial support for Rubin Observatory comes from the National Science Foundation (NSF) through Cooperative Agreement No. 1258333, the Department of Energy (DOE) Office of Science under Contract No. DE-AC02-76SF00515, and private funding raised by the LSST Corporation. The NSF-funded Rubin Observatory Project Office for construction was established as an operating center under management of the Association of Universities for Research in Astronomy (AURA). The DOE-funded effort to build the Rubin Observatory LSST Camera (LSSTCam) is managed by the SLAC National Accelerator Laboratory (SLAC).
The National Science Foundation (NSF) is an
independent federal agency created by Congress
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NSF and DOE will continue to support Rubin Observatory in its Operations phase. They will also provide support for scientific research with LSST data.
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