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

: 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 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).

: 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.

: 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.

: 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

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

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:

999 Part 1(1).mp4 -

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

: 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 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

: 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.

: 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. : Recognizes if a worker is facing away

: 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

Because real-world collision data is dangerous and expensive to collect, researchers used a approach: : By using the known size of objects

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: