@article{ref1,
title="The detection system for a danger state of a collision between construction equipment and workers using fixed CCTV on construction sites",
journal="Sensors (Basel)",
year="2023",
author="Seong, Jaehwan and Kim, Hyung-Soo and Jung, Hyung-Jo",
volume="23",
number="20",
pages="-",
abstract="According to data from the Ministry of Employment and Labor in Korea, a significant portion of fatal accidents on construction sites occur due to collisions between construction workers and equipment, with many of these collisions being attributed to worker negligence. This study introduces a method for accurately localizing construction equipment and workers on-site, delineating areas prone to collisions as 'a danger area of a collision', and defining collision risk states. Utilizing advanced deep learning models which specialize in object detection, video footage obtained from strategically placed closed-circuit television (CCTV) cameras across the construction site is analyzed. The positions of each detected object are determined using transformation or homography matrices representing the conversion relationship between a sufficiently flat reference plane and image coordinates. Additionally, 'a danger area of a collision' is proposed for evaluating equipment collision risk based on the moving equipment's speed, and the validity of this area is verified. Through this, the paper presents a system designed to preemptively identify potential collision risks, particularly when workers are located within the 'danger area of a collision', thereby mitigating accident risks on construction sites.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s23208371",
url="http://dx.doi.org/10.3390/s23208371"
}