[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]
Copy

Landslide Recognition After the 2021 Haiti MS7.2 Earthquake Based on the Improved YOLOv4 Algorithm

References:

巨袁臻,许强,金时超,等.2020.使用深度学习方法实现黄土滑坡自动识别[J].武汉大学学报(信息科学版),45(11):1747-1755.
林齐根,邹振华,祝瑛琦,等.2017.基于光谱、空间和形态特征的面向对象滑坡识别[J].遥感技术与应用,32(5):931-937.
刘旭霞,秦芮.2021.7.2级地震令海地“摇摇欲坠”[N].环球时报,2021-08-16(2).
牛全福,程维明,兰恒星,等.2010.玉树地震滑坡灾害的遥感提取与分布特征分析[C]//中国灾害防御协会.全国突发性地质灾害应急处置与灾害防治技术高级研讨会论文集.北京:中国灾害防御协会,7.
许冲,戴福初,陈剑,等.2009.汶川MS8.0地震重灾区次生地质灾害遥感精细解译[J].遥感学报,13(4):754-762.
Benjdira B,Khursheed T,Koubaa A, et al.2019.Car detection using unmanned aerial vehicles:Comparison between faster r-cnn and yolov3[C]//2019 1st International Conference on Unmanned Vehicle Systems-Oman(UVS).IEEE,1-6.
Bialas J,Oommen T,Rebbapragada U, et al.2016.Object-based classification of earthquake damage from high-resolution optical imagery using machine learning[J].Journal of Applied Remote Sensing,10(3):036025.
Bochkovskiy A,Wang C Y,Liao H Y M.2020.Yolov4:Optimal speed and accuracy of object detection[J].arXiv preprint arXiv:2004.10934.
Cheng L,Li J,Duan P, et al.2021a.A small attentional YOLO model for landslide detection from satellite remote sensing images[J].Landslides,18(8):2751-2765.
Cheng Z,Gong W,Tang H, et al.2021b.UAV photogrammetry-based remote sensing and preliminary assessment of the behavior of a landslide in Guizhou,China[J].Engineering Geology,289:106172.
Everingham M,Van Gool L,Williams C K I, et al.2010.The pascal visual object classes(voc)challenge[J].International journal of computer vision,88(2):303-338.
He K,Zhang X,Ren S, et al.2015.Spatial pyramid pooling in deep convolutional networks for visual recognition[J].IEEE transactions on pattern analysis and machine intelligence,37(9):1904-1916.
Howard A G,Zhu M,Chen B, et al.2017.Mobilenets:Efficient convolutional neural networks for mobile vision applications[J].arXiv preprint arXiv,doi:10.48550/arXiv.1704.04861.
Howard A,Sandler M,Chu G, et al.2019.Searching for MobileNetv3[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision,1314-1324.
Huang Y,Zhao L.2018.Review on landslide susceptibility mapping using support vector machines[J].Catena,165:520-529.
Kavzoglu T,Colkesen I,Sahin E K.2019.Machine learning techniques in landslide susceptibility mapping:a survey and a case study[J].Landslides:Theory,practice and modelling,283-301,doi:10.10071978-3-319-77377-3-13.
Keyport R N,Oommen T,Martha T R, et al.2018.A comparative analysis of pixel-and object-based detection of landslides from very high-resolution images[J].International journal of applied earth observation and geoinformation,64:1-11.
Lin T Y,Goyal P,Girshick R, et al.2017.Focal loss for dense object detection[C]//Proceedings of the IEEE international conference on computer vision,2980-2988.
Liu W,Anguelov D,Erhan D, et al.2016.Ssd:Single shot multibox detector[C]//European conference on computer vision.Springer,Cham,21-37.
Micheletti N,Foresti L,Robert S, et al.2014.Machine learning feature selection methods for landslide susceptibility mapping[J].Mathematical geosciences,46(1):33-57.
Redmon J,Divvala S,Girshick R, et al.2016.You only look once:Unified,real-time object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition,779-788.
Redmon J,Farhadi A.2018.Yolov3:An incremental improvement[J].arXiv preprint arXiv:1804.02767.
Ren S,He K,Girshick R, et al.2015.Faster r-cnn:Towards real-time object detection with region proposal networks[J].Advances in neural information processing systems,doi:10.48550/arXiv.1506.01497.
Tien B D,Ho T C,Revhaug I, et al.2014.Landslide susceptibility mapping along the national road 32 of Vietnam using GIS-based J48 decision tree classifier and its ensembles[M]//Cartography from pole to pole.Springer,Berlin,Heidelberg,303-317.
Wang C Y,Liao H Y M,Wu Y H, et al.2020.CSPNet:A new backbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops,390-391.
Wang D,He D.2019.Recognition of apple targets before fruits thinning by robot based on R-FCN deep convolution neural network[J].Trans Chin Soc Agric Eng,35:156-163.
Similar References:

Memo

-

Last Update: 2023-03-10

Online:132       Total Traffic Statistics:4820444

Website Copyright:Editorial Office of Journal of Seismological Research
Address:148 Beichen Street, North District of Kunming City, Yunnan, China Tel: 86-0871-3355074