Detecting Building Façade Damage Caused by Earthquake Using CBAM-Improved Mask R-CNN

  Objectives  Building damage information can provide an important basis for the decision making of rapid post-earthquake assessment. Traditional building damage detection techniques mainly focus on the roof surface, thus many damaged buildings with an intact roof surface but collapsed middle floors may be neglected. We propose a method of building façade damage detection based on deep learning and multiresolution segmentation algorithm.

  Methods  The method which integrates the instance segmentation and multiresolution segmentation algorithm is applied to detecting the post-earthquake building façade damage. The first thing is to collect the ground images of post-earthquake buildings in the field and perform the data augmentation. Secondly, we use the convolutional block attention module (CBAM) to improve Mask R-CNN. Then the dataset is input to the improved model for training, and finally a multiresolution segmentation algorithm is adopted to post-process the building façade damage detection results output by the CBAM-Improved Mask R-CNN.

  Results  The experimental results show: (1) Collecting ground images of buildings in the field and performing image augmentation can effectively guarantee the necessary training sample size of the instance segmentation model. (2) The Mask R-CNN improved by CBAM attention mechanism significantly improves the post-earthquake building facade damage detection capabilities, which realizes the precise extraction of damage information from complex building façade backgrounds. (3) In addition, using the multiresolution segmentation algorithm to post-process the building facade damage detection results can obviously solve the blurred boundary problems caused by the accumulation of convolutional layers.

  Conclusions  The proposed method can significantly improve the capability of post-earthquake building façade damage detection when compared to the traditional methods, which also raises the Mask R-CNN's accuracy, precision, recall and F₂-score to a certain degree. It can be inferred that the proposed method has the strong potential to be applied to the post-earthquake building façade damage detection and therefore provides an important technical means for detecting the comprehensive and detailed building damage detection caused by earthquake.