摘要:建筑业的施工安全长久以来都是社会热点问题，然而消除施工安全隐患却始终是难点。随着无人机和人工智能技术的发展，将图像采集与识别技术应用到建筑施工安全防治领域是当前的研究热点之一。高空坠落事故是施工安全事故占比最大的事故类型，临边防护的缺失是导致高空坠落事故频发的主要原因之一。本文利用深度学习框架搭建了5种主流卷积神经网络模型，对无人机采集的3 600张施工现场图像数据集进行训练与测试。实验结果表明，5种模型识别安全检测中临边护栏的准确率皆已达到90%以上，经对比分析得出MobileNet模型对临边护栏识别效果最佳。研究结果验证了图像识别的CNN算法与无人机技术应用于快速识别施工现场安全隐患的有效性和可行性，有助于消除施工现场不安全隐患，提升建筑业的安全管理水平。

Abstract: Safety in the construction industry has become a hot issue for a long time, but it is always difficult to eliminate hidden dangers in construction safety. With the development of drones and artificial intelligence technology, the application of image acquisition and identification technology to the field of building construction safety prevention and control is one of the current research hotspots. High-altitude fall accidents are the type of accidents with the largest proportion of construction safety accidents. The lack of safety guardrail is part of the main reasons for frequent high-altitude fall accidents. This paper uses a deep learning framework to build 5 mainstream convolutional neural network models to train and test 3600 construction site image data sets collected by UAV(unmanned aerial vehicle). The experimental results demonstrate that the accuracy of the safety guardrails in the five models for identifying security detection has reached more than 90%. After comparative analysis, it is concluded that the Mobilenet model has the best recognition effect for the border guardrails. The research results verify the effectiveness and feasibility of the image recognition CNN algorithm and UAV technology for quickly identifying potential safety hazards at the construction site, which will help eliminate potential safety hazards at the construction site and improve the safety management level of the construction industry.