摘要:建筑工程施工现场人员密集、环境复杂，存在大量危险源和危险区域，违规进入这些区域会带来重大安全隐患甚至引发安全事故。现有的施工现场监控主要依靠专职安全员巡查，存在耗时、耗力、预警不及时等问题。为提高施工现场监控效率，充分利用已有摄像头资源，本文基于双目立体视觉技术和深度学习，提出了一种施工机械危险区域侵入检测方法。首先梳理相关文献，确定危险区域的划分方法；然后通过目标检测算法检测施工人员和机械的像素定位，利用双目立体视觉技术计算人机距离，并改进了人机距离判定方法，根据检测距离判断是否侵入危险区域；最后通过施工现场综合模拟实验验证，结果显示该方法能够准确识别施工人员侵入危险区域的行为，并提供预警信息，有效提高施工现场的监控效率和安全性。

Abstract: Construction sites are often crowded, with poor working environments, many sources of danger, and hazardous areas. Unauthorized entry into these areas poses significant safety risks and can lead to accidents. Traditional construction site monitoring relies on full-time safety officers patrolling the site, which is time-consuming, labor-intensive, and results in untimely warnings. To improve site monitoring efficiency and make full use of existing cameras, this paper proposes a construction hazardous area intrusion detection method based on binocular stereo vision technology and deep learning. First, relevant literature is reviewed to determine the methods for establishing and dividing dangerous areas. A target detection algorithm is then used to locate construction personnel and machinery, and binocular stereo vision technology calculates the distance between them. The method for determining the man-machine distance is improved to judge whether there is intrusion into dangerous areas based on the detected distance. A comprehensive construction site simulation experiment verifies the method, and results show that it accurately detects intrusion behavior and provides early warning information, significantly improving site monitoring efficiency and safety.