: 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.
: Recognizes if a worker is facing away or kneeling, which increases risk. 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.
Because real-world collision data is dangerous and expensive to collect, researchers used a approach: : The video frames were used to train
: 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
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: : By using the known size of objects
: Scenarios were built in Unity 3D to mimic real-world construction tasks, such as collaborative excavation.