Seong Jaehwan, Kim Hyung-Soo, Jung Hyung-Jo
Department of Civil and Environmental Engineering, KAIST, Daejeon 34141, Republic of Korea.
Sensors (Basel). 2023 Oct 10;23(20):8371. doi: 10.3390/s23208371.
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.
根据韩国雇佣劳动部的数据，建筑工地上很大一部分致命事故是由于建筑工人与设备之间的碰撞造成的，其中许多碰撞归因于工人的疏忽。本研究介绍了一种在现场精确定位建筑设备和工人的方法，将容易发生碰撞的区域划定为“碰撞危险区域”，并定义碰撞风险状态。利用专门用于目标检测的先进深度学习模型，分析从建筑工地各处战略性放置的闭路电视（CCTV）摄像机获取的视频画面。使用表示足够平坦的参考平面与图像坐标之间转换关系的变换或单应性矩阵来确定每个检测到的物体的位置。此外，基于移动设备的速度提出了“碰撞危险区域”以评估设备碰撞风险，并验证了该区域的有效性。通过此方法，本文提出了一种系统，旨在预先识别潜在的碰撞风险，特别是当工人位于“碰撞危险区域”内时，从而降低建筑工地的事故风险。
