论文标题
一种基于R-CNN的新方法，用于改进滑坡检测
A New Mask R-CNN Based Method for Improved Landslide Detection
论文作者
论文摘要
本文通过利用蒙版R-CNN的能力来通过使用基于像素的分割来识别对象布局的能力，以及用于训练提出的模型的转移学习，从而提出了一种新颖的滑坡检测方法。创建了一个包含滑坡和非线坡图像的160个元素的数据集。所提出的方法包括三个步骤：（i）增加训练图像样本以增加训练数据的数量，（ii）对图像样本有限的微调，以及（iii）通过在考虑的landslide图像上对算法，召回和F1测量的算法评估，通过采用Resnet-50和101作为backbone模型。实验结果令人鼓舞，因为提出的方法达到精度等于1.00，召回0.93和F1度量0.97，当使用RESNET-101用作骨干模型，并且用作训练样品的数量较低。拟议的算法对于土地使用规划师和丘陵地区的政策制定者可能有用，那里间歇性斜率变形需要在计划前将滑坡检测作为先决条件。
This paper presents a novel method of landslide detection by exploiting the Mask R-CNN capability of identifying an object layout by using a pixel-based segmentation, along with transfer learning used to train the proposed model. A data set of 160 elements is created containing landslide and non-landslide images. The proposed method consists of three steps: (i) augmenting training image samples to increase the volume of the training data, (ii) fine tuning with limited image samples, and (iii) performance evaluation of the algorithm in terms of precision, recall and F1 measure, on the considered landslide images, by adopting ResNet-50 and 101 as backbone models. The experimental results are quite encouraging as the proposed method achieves Precision equals to 1.00, Recall 0.93 and F1 measure 0.97, when ResNet-101 is used as backbone model, and with a low number of landslide photographs used as training samples. The proposed algorithm can be potentially useful for land use planners and policy makers of hilly areas where intermittent slope deformations necessitate landslide detection as prerequisite before planning.
