摘要:土木工程施工现场是一个复杂多变且事故发生率较高的作业环境，同一个工程场地存在着多个危险区域。对该区域传统的管理方式是派专职人员进行看守和管理，这种人工管理的方式易出错且不能够及时发现人员的进入情况。对于动态危险区域，操作人员在操作机械的同时还要兼顾周围环境情况，这不仅会降低工作效率且不利于发现该区域存在的工人。为了解决这个问题和提高监管的效率，计算机视觉的融入将会是很好的选择，该方法首先需要根据相关规章制度去确定危险区域，然后在合适的位置布置摄像头，最后运用YOLOv3(You Only Look Once，是一种快速和准确的实时对象检测算法，发展到YOLOv3实现了算法上的突破，在精度和速度上也实现了质的飞越)目标识别算法实现智能监管。本文介绍了该算法的基本原理和具体的目标识别实现途径，并针对危险区域的范围不同设计了两种训练集的制作方式，最后用实验去验证该方法的可行性与准确性，结果表明，该方法对于工人的识别具有较高的正确率，故把该方法用于危险区域的工人识别将会大大降低事故发生概率，弥补了单纯人工监管的缺陷，丰富了安全管理的手段。

Abstract: The construction site is a complex and changeable working environment with a high accident rate. There are multiple hazardous areas on the same engineering site. The traditional management method for this area is to send full-time personnel to guard and manage. This manual management method is prone to errors and cannot timely detect the entry of personnel in time.For dynamic hazardous areas, operators must take consideration of the surrounding environment while operating the machinery, which will not only reduce work efficiency but also be unfavorable for finding workers in the area. In order to solve this problem and to improve the efficiency of supervision, the integration of computer vision will be a good choice. This method first needs to determine the hazardous area according to relevant rules and regulations, then arrange the camera at the appropriate position, and finally use the YOLOv3(You Only Look Once, is a fast and accurate real-time object detection algorithm, developed to YOLOv3 to achieve a breakthrough in the algorithm, and also achieved a qualitative leap in accuracy and speed)target recognition algorithm to achieve Intelligent supervision. This article introduces the basic principle of the algorithm and the specific realization method of target recognition, and according to the different scope of the hazardous area, two methods of making training sets are being designed.Finally, experiments are used to verify the feasibility and accuracy of the method. The results show that the method has a higher accuracy rate for worker identification. Therefore, applying this method to worker identification in hazardous areas will greatly reduce the probability of accidents and make up for the shortcomings of purely manual supervision enrich the means of safety management.