999 Part 1(1).mp4 Info

: 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

: 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. 999 Part 1(1).mp4

Because real-world collision data is dangerous and expensive to collect, researchers used a approach: : To save time, researchers used the virtual

The video is part of a study that addresses the high rate of accidents in the construction industry. Unlike traditional sensors that fire an alarm whenever any object is near, DCAS uses a to evaluate risk dynamically based on: Unlike traditional sensors that fire an alarm whenever

: 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 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.

: Adjusts risk based on where the camera is mounted on the machine (e.g., blind spots). How the Video Was Created