基于深度学习的村镇砖(混)木房屋识别

破坏性地震发生后, 相较按照抗震设防标准建设的城市公共设施和居民住宅, 广大无抗震设防的村镇居民自建房屋, 更易发生倒塌甚至完全损毁. 以往地震灾情预评估、地震灾害风险调查、地震重点危险区调研, 依靠专家现场踏勘, 确定不同结构类型建筑物数量及所占比例. 本研究借助深度学习和倾斜摄影技术, 进行砖(混)木结构房屋识别, 郯庐断裂带山东境内砖(混)木房屋影像制作数据集, 训练得到Faster R-CNN模型, 该区域内砖(混)木房屋识别平均精度为91.868%. 结果表明, 本文方法能够对砖(混)木房屋进行有效检测, 可应用于地震行业开展震前、震后各类现场工作, 提高政府部门应急管理能力.

Recognizing Brick (Concrete) Wood Rural House Based on Deep Learning

After the occurrence of destructive earthquake, compared with various urban public facilities and residential buildings built in accordance with the relatively seismic fortification standards, the vast number of villages and towns without seismic fortification houses are more likely to collapse or even completely damage. In the past, earthquake disaster risk investigation and disaster assessment relied on the field survey of experts to determine the number and proportion of buildings of different structural types. In this study, brick (concrete) wood structure houses are identified by deep learning and photography technology. The Faster R-CNN model is trained for the data set of brick (mixed) wood houses in Shandong Province of Tan Lu fault zone, with an average accuracy of 91.868%. The results show that this method can effectively detect brick (concrete) wood houses, and can be applied to earthquake disaster pre-assessment, earthquake disaster risk investigation, earthquake key-risk area investigation, and other related work.