999 Part 1(1).mp4 -

: 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.

The full research and technical details can be found in the article Dynamic Collision Alert System for Collaboration of Construction Equipment and Workers published in Buildings (MDPI). 999 Part 1(1).mp4

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

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

: 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.

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: : Adjusts risk based on where the camera

: 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.