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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.

: 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. 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.

: Scenarios were built in Unity 3D to mimic real-world construction tasks, such as collaborative excavation. : 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:

: 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

: Recognizes if a worker is facing away or kneeling, which increases risk.