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: 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

: Distinguishes between workers, excavators, and forklifts. 999 Part 1(1).mp4

: By using the known size of objects and camera focal lengths, the system can estimate the distance of a worker or machine within a small margin of error. : To save time, researchers used the virtual

: 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. : The video frames were used to train

: 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 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.

Because real-world collision data is dangerous and expensive to collect, researchers used a approach: