陈鹏, 汪本康, 高飒, 童小华, 卢文胜, 徐胜华. 利用ResNet进行建筑物倒塌评估[J]. 武汉大学学报 ( 信息科学版), 2020, 45(8): 1179-1184. DOI: 10.13203/j.whugis20200135
引用本文: 陈鹏, 汪本康, 高飒, 童小华, 卢文胜, 徐胜华. 利用ResNet进行建筑物倒塌评估[J]. 武汉大学学报 ( 信息科学版), 2020, 45(8): 1179-1184. DOI: 10.13203/j.whugis20200135
CHEN Peng, WANG Benkang, GAO Sa, TONG Xiaohua, LU Wensheng, XU Shenghua. Building Collapse Assessment with Residual Network[J]. Geomatics and Information Science of Wuhan University, 2020, 45(8): 1179-1184. DOI: 10.13203/j.whugis20200135
Citation: CHEN Peng, WANG Benkang, GAO Sa, TONG Xiaohua, LU Wensheng, XU Shenghua. Building Collapse Assessment with Residual Network[J]. Geomatics and Information Science of Wuhan University, 2020, 45(8): 1179-1184. DOI: 10.13203/j.whugis20200135

利用ResNet进行建筑物倒塌评估

Building Collapse Assessment with Residual Network

  • 摘要: 灾害发生之后快速对建筑物倒塌损毁程度进行准确评估对减轻灾害损失具有重要意义。将深度学习技术应用于建筑物倒塌评估,在残差网络(residual network,ResNet)预训练网络的基础上,通过深度迁移学习的方法,按照建筑物破坏评估标准建立了用于建筑物倒塌评估的分类器模型,利用图像数据结构损伤识别比赛(PEER Hub ImageNet Challenge,PHI)的开放数据训练获得了相对最优的模型参数,并用该模型开展了建筑物倒塌评估实验。实验结果表明,基于ResNet的建筑物倒塌评估模型对建筑物倒塌状态具有良好的识别效果和应用潜力。

     

    Abstract:
      Objectives  The building collapse is the main cause of casualties and economic losses in all kinds of disasters. Accurate assessment of the collapse of buildings is an important basis for disaster rescue and government decision-making, and plays an important role in the proper placement of the victims, the protection of the lives and property of the victims. Therefore, the rapid assessment of building collapse after disaster is a research hotspot in the field of remote sensing and civil engineering. The main objectives of this study was to apply the deep learning technology to automatically identify and assess building collapse and structural damage with on-site images.
      Methods   The building collapse assessment method used in this paper is ResNet (residual network). A ResNet-50 architecture was implemented in TensorFlow, which consists of 5 stages each with a convolution and identity block, 3 convolution layers in each convolution block and 3 convolution layers in each identity block. According to the building damage assessment standard developed by the China Earthquake Administration, the evaluation output of this model is adjusted to three types: Completely collapsed, semi-collapsed, and non-collapse. In the data augmentation process, geometry transformation and blur processing were used to simulate different shooting environments, to reduce the overfitting of the classifier model by enhancing the training image. Through data expansion, the number of training data has been increased by at least 10 times on the basis of PEER Hub ImageNet Challenge open dataset. In the process of transfer learning, the gradient descent algorithm was used to adjust and optimize the parameters of the neural network. The results of this method were evaluated by using accuracy and Kappa coefficient.
      Results   Finally, in the building collapse assessment experiment, the classification accuracy of the assessment model reached 77.23% and the Kappa coefficient was 0.65; in the structural damage assessment experiment, the classification accuracy of the assessment model reached 77% and the Kappa coefficient was 0.693.The proposed collapse classification successfully attains a relatively high rate for true-positives.Overall, this method performed well. This method shows great promise in supporting on-site decision-making for the rapid accurate assessment of building collapse after disaster.
      Conclusions   A novel automatic classification method for post-disaster building collapse evaluation is proposed,which meets the buil- ding damage evaluation standard. The method is demonstrated on a specific example classification focused on collapse classification and structural damage type identification.The experiment results show great pro- mise in the rapid accurate assessment of building collapse after disaster. In the future we plan to improve the model to enhance the ability to extract subtle features in the image.

     

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