首页|基于改进DeeplabV3+的滑坡识别方法优化

基于改进DeeplabV3+的滑坡识别方法优化

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  • NETL
  • NSTL
  • 万方数据
对于滑坡的研究主要包括两个方面，滑坡识别和滑坡灾害的风险评估，而滑坡的识别是所有滑坡研究的基础。随着计算机视觉的发展，将深度学习应用于滑坡识别的研究也在相继展开。通过残差接入通道注意力机制以及设计一个多尺度特征融合模块以融合不同尺度的特征，提出了一种基于deeplabV3+的改进的滑坡识别方法。根据制作的包含 1227 张滑坡图像的数据集，在此数据集上进行了对比实验与消融实验，将数据集以比例9:1 分为训练集和验证集，结果表明在验证集上提出的优化方法效果最佳，其中平均交并比mIoU达到了 84。12％、PA达到了94。23％。相比其它网络中表现最好的网络HR-NetV2，评价指标mIoU也提高了2。54％、PA提高了1。01％。
Optimization of landslide identification Method Based on Improved DeeplabV3+
The study on landslides mainly includes two aspects,landslide identification and landslide hazard risk assessment,and landslide identification is the basis of all landslide research.With the development of computer vi-sion,the application of deep learning to landslide recognition has also been carried out one after another.In this pa-per,an improved landslide recognition method based on deeplabV3+is proposed by using the residual access channel attention mechanism and designing a multi-scale feature fusion module to fuse features at different scales.According to the dataset containing 1227 landslide images,comparative experiments and ablation experiments were carried out on this dataset,and the dataset was divided into training set and validation set with a ratio of 9:1.And the results show that the optimization method proposed in this paper has the best effect on the validation set,in which the average in-tersection union ratio of mIoU reaches 84.12％ and PA reaches 94.23％ .Compared with other best-performing net-works,the HRNetV2 evaluation index mIoU also increases by 2.54％ and PA by 1.01％ .

Landslide imageNeural networksCoordinate attention mechanicalMulti scale featureFeature fusion

万逸轩、黄建华、孙希延、罗明明

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桂林电子科技大学信息与通信学院,广西 桂林 541004

桂林电子科技大学广西精密导航技术与应用重点实验室,广西 桂林 541004

滑坡图像 神经网络 通道注意力机制 多尺度特征 特征融合

国家自然科学基金国家自然科学基金国家自然科学基金桂林市科技局桂林市国家可持续发展议程创新示范区建设重点项目广西自然科学基金广西自然科学基金

61861008620610106216100720190219-12018GXNSFAA2940542019GXNSFBA245072

2024

计算机仿真
中国航天科工集团公司第十七研究所

计算机仿真

CSTPCD
影响因子：0.518
ISSN：1006-9348
年,卷(期)：2024.41(9)
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