中国安全科学学报 ›› 2017, Vol. 27 ›› Issue (5): 1-6.doi: 10.16265/j.cnki.issn1003-3033.2017.05.001

• 安全科学技术基础学科 •    下一篇

基于深度学习的施工安全泛场景数据获取方法

佟瑞鹏 副教授, 陈策, 崔鹏程, 傅贵 教授, 安宇 高级工程师   

  1. 中国矿业大学(北京) 资源与安全工程学院,北京 100083
  • 收稿日期:2017-03-05 修回日期:2017-04-20 出版日期:2017-05-20 发布日期:2020-10-30
  • 作者简介:佟瑞鹏 (1977—),男,黑龙江穆棱人,博士,副教授,主要从事行为安全与管理、风险建模与评估、公共安全与健康等方面的研究。E-mail:tongrp@cumtb.edu.cn。
  • 基金资助:
    国家自然科学基金资助(51674268)。

Deep learning method for processing pan-scene data on construction safety

TONG Ruipeng, CHEN Ce, CUI Pengcheng, FU Gui, AN Yu   

  1. College of Resources & Safety Engineering,China University of Mining &Technology (Beijing),Beijing 100083,China
  • Received:2017-03-05 Revised:2017-04-20 Online:2017-05-20 Published:2020-10-30

摘要: 为实现隐患场景识别和施工安全泛场景数据智能化处理,在分析场景构成因素基础上,将施工场景划分为不安全行为场景和不安全物态场景2类。结合图像语义层次的划分,应用深度学习方法提取对象语义、空间关系语义、场景语义和行为语义,进而依据施工安全泛场景数据理论,得到图像语义信息与泛场景数据对应关系。选取建筑施工现场照片为样本源,采集施工不安全行为场景数据,验证应用基于深度学习的方法处理泛场景数据的可行性。结果表明:通过深度学习提取图像语义,可以自动采集不安全行为和不安全场景泛物态数据,提高场景数据处理效率。

关键词: 深度学习, 施工场景, 图像语义, 泛场景数据, 不安全行为

Abstract: In order to identify potential accidents at construction scenes and process the pan-scene data on construction safety intelligently, the construction scenes were divided into two groups, unsafe behavior scenes and unsafe state scenes, on the basis of the scene factors. Using deep learning method, the object semantics, spatial relation semantics, scene semantics and behavior semantics were extracted from the pan-scene data on the basis of the division of image semantics hierarchies. Then, the correspondences were obtained between image semantics and the pan-scene data according to the theory of pan-scene data on construction safety. Building construction site photos were collected as the sample source to acquire the scene data of unsafe behavior in construction, and the feasibility of processing the pan-scene data by using a deep learning method was validated by analyzing the scene data of unsafe behavior in construction. The result shows that the pan-scene data can be acquired antomatically and processed efficiently through extracting the image semantics with a deep learning method.

Key words: deep learning, construction scene, image semantics, pan-scene data, unsafe behavior

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