Detecting damaged buildings after an earthquake as quickly as possible is important for emergency teams to reach these buildings and save the lives of many people. Today, damaged buildings after the earthquake are carried out by the survivors contacting the authorities or using some air vehicles such as helicopters. In this study, AI-based systems were tested to detect damaged or destroyed buildings by integrating into street camera systems after unexpected disasters. For this purpose, we have used VGG-16, VGG-19, and NASNet convolutional neural network models which are often used for image recognition problems in the literature to detect damaged buildings. In order to effectively implement these models, we have first segmented all the images with the K-means clustering algorithm. Thereafter, for the first phase of this study, segmented images labeled “damaged buildings” and “normal” were classified and the VGG-19 model was the most successful model with a 90% accuracy in the test set. Besides, as the second phase of the study, we have created a multiclass classification problem by labeling segmented images as “damaged buildings,” “less damaged buildings,” and “normal.” The same three architectures are used to achieve the most accurate classification results on the test set. VGG-19 and VGG-16, and NASNet have achieved considerable success in the test set with about 70%, 67%, and 62% accuracy, respectively.

尽快发现地震后受损的建筑物对于紧急救援队到达这些建筑物并挽救许多人的生命非常重要。今天，地震发生后受损的建筑物是由幸存者联系当局或使用诸如直升机之类的飞行器进行的。在这项研究中，对基于AI的系统进行了测试，以通过在意外灾难后将其集成到路灯摄像头系统中来检测受损或毁坏的建筑物。为此，我们使用了VGG-16，VGG-19和NASNet卷积神经网络模型，这些模型通常用于文献中的图像识别问题以检测受损的建筑物。为了有效地实现这些模型，我们首先使用K-means聚类算法对所有图像进行了分割。之后，在本研究的第一阶段，分类标记为“损坏的建筑物”和“正常”的图像，VGG-19模型是最成功的模型，其测试集中的准确率达到90％。此外，作为研究的第二阶段，我们通过将分段图像标记为“损坏的建筑物”，“损坏较少的建筑物”和“正常”来创建多类分类问题。相同的三种体系结构用于在测试集上获得最准确的分类结果。VGG-19和VGG-16以及NASNet在测试仪中均取得了相当大的成功，其准确率分别约为70％，67％和62％。我们通过将分段图像标记为“损坏的建筑物”，“损坏较少的建筑物”和“正常”来创建多类分类问题。相同的三种体系结构用于在测试集上获得最准确的分类结果。VGG-19和VGG-16以及NASNet在测试仪中均取得了相当大的成功，其准确率分别约为70％，67％和62％。我们通过将分段图像标记为“损坏的建筑物”，“损坏较少的建筑物”和“正常”来创建多类分类问题。相同的三种体系结构用于在测试集上获得最准确的分类结果。VGG-19和VGG-16以及NASNet在测试仪中均取得了相当大的成功，其准确率分别约为70％，67％和62％。