999 Part 1(1).mp4 <2026 Release>

: 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 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 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

: Recognizes if a worker is facing away or kneeling, which increases risk. : The system significantly decreased the number of

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

: Distinguishes between workers, excavators, and forklifts. : Adjusts risk based on where the camera

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: