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[1]FU Rao,HE Jing,LIU Gang.Landslide Recognition After the 2021 Haiti MS7.2 Earthquake Based on the Improved YOLOv4 Algorithm[J].Journal of Seismological Research,2023,46(02):300-307.[doi:10.20015/j.cnki.ISSN1000-0666.2023.0012 ]

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Landslide Recognition After the 2021 Haiti M_S7.2 Earthquake Based on the Improved YOLOv4 Algorithm

Journal of Seismological Research[ISSN 1000-0666/CN 53-1062/P] Volume: 46 Number of periods: 2023 02 Page number: 300-307 Column: Public date: 2023-03-10

Title:: Landslide Recognition After the 2021 Haiti M_S7.2 Earthquake Based on the Improved YOLOv4 Algorithm

Author(s):: FU Rao¹; HE Jing¹; LIU Gang¹; 2; (1.School of Earth Sciences,Chengdu University of Technology,Chengdu 610059,Sichuan,China)(2.State Key Laboratory of Geological Disaster Prevention and Geological Environment Protection,Chengdu University of Technology,Chengdu 610059,Sichuan,China)

Keywords:: the YOLOv4 algorithm; the Haiti Earthquake; landslide identification; high resolution image

CLC:: P642.22

DOI:: 10.20015/j.cnki.ISSN1000-0666.2023.0012

Abstract:: Rapid identification of seismic landslides is important for emergency rescue and loss assessment.On August 14th,2021,a 7.2-magnitude earthquake occurred in Haiti,inducing a large number of landslides.In this paper,the improved YOLOv4 algorithm is used to identify the landslides induced by the Haiti M_S7.2 Earthquake using the domestically produced high-fraction 2 images as the data source.To improve the recognition efficiency of the model,the backbone network CSPDarknet53 of Yolov4 is replaced with MobileNetv3,and the ordinary convolution in the YOLOv4 is replaced with depth-separable convolution to optimize the model parameters and network structure.The improved YOLOv4 algorithm achieves 91.37% of the accuracy of target recognition,6.19 f/s(5.24%)higher than the detection speed of the normal YOLOv4,providing more reliable data for emergency rescue and disaster assessment.

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Last Update: 2023-03-10