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

: 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. 999 Part 1(1).mp4

Because real-world collision data is dangerous and expensive to collect, researchers used a approach: : Adjusts risk based on where the camera

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). The video is part of a study that

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:

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

: 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.