基于卷积神经网络的工程扰动区地质灾害识别（英文）

Detection of geohazards caused by human disturbance activities based on convolutional neural networks

摘要

工程扰动是诱发地质灾害的主要原因之一，目前，对道路工程的生态影响评价存在标准不一、指标难定量的问题。从图像检测的角度出发，通过识别道路沿线影像中植被裸露区域可直观反映工程扰动对环境的破坏程度。为此，提出一种基于卷积神经网络的环境破坏检测模型：采用50层残差网络进行特征图提取，通过迁移学习优化初始参数，并以神农架国家森林公园道路沿线的坍塌破坏与滑坡破坏自建数据集为例进行验证。结果表明，坍塌破坏的AP值为0.470 3,滑坡破坏的AP值为0.480 9。与YOLOv3模型相比，本模型虽牺牲了一定检测速度，但在坍塌与滑坡识别精度上更具优势。

Abstract

Human disturbance activities is one of the main reasons for inducing geohazards. Ecological impact assessment metrics of roads are inconsistent criteria and multiple. From the perspective of visual observation, the environment damage can be shown through detecting the uncovered area of vegetation in the images along road. To realize this, an end-to-end environment damage detection model based on convolutional neural network is proposed. A 50-layer residual network is used to extract feature map. The initial parameters are optimized by transfer learning. An example is shown by this method. The dataset including cliff and landslide damage are collected by us along road in Shennongjia national forest park. Results show 0. 470 3 average precision(AP) rating for cliff damage and 0. 480 9 average precision(AP) rating for landslide damage. Compared with YOLOv3, our model shows a better accuracy in cliff and landslide detection although a certain amount of speed is sacrificed.

关键词

卷积神经网络 / 目标检测 / 环境破坏 / 坍塌 / 滑坡

Key words

convolutional neural network / detection / environment damage / cliff / landslide

基金资助

湖北省博士后创新实践岗位项目“基于粘结块体-合成岩体方法的硬质围岩水工隧洞稳定研究”(2023CXGW01)