Enhance the Accuracy of Landslide Detection in UAV Images Using an Improved Mask R-CNN Model: A Case Study of Sanming, China

Lu, Yun; Zhang, Xinxin; Zheng, Yuchao; Wang, Da‐Han; Hua, Lijuan

doi:10.3390/s23094287

Cited by 9 publications

(7 citation statements)

References 52 publications

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“…In addition, recently, UAVs have been being used in completely new areas: monitoring crop diseases, landslides, mountain ranges, etc. [29][30][31]. The approach, with minimal changes, can be adapted to assess the effectiveness of UAVs in these areas as well.…”

Section: Discussionmentioning

confidence: 99%

Evaluation of Unmanned-Aerial-Vehicle-Integrated Control System Efficiency on the Basis of Generalized Multiplicative Criterion

Vititin,

Kalyagin,

Kolesnichenko

2023

Inventions

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An unmanned aerial vehicle (UAV)-integrated control system is a set of functionally independent subsystems of the ground and space segments interacting with each other under the conditions of the stochastic nature of the external environment. There is an approach to evaluating its effectiveness based on a generalized multiplicative criterion, which takes into account the features of this system to the maximum extent. It is proposed to single out two particular criteria that characterize the reliability of a UAV and the effectiveness of the control system in relation to it. At the same time, the generalized criterion is a multiplicative convolution based not on the triangular-norm (t-norm) of the particular criterion, but of its correspondence functions, which in a certain way reflect its significance. It is shown that in the particular case of linear dependence of the correspondence functions, the generalized criterion coincides with the classical multiplicative convolution in the form of product of event probabilities. The proposed approach with minimal changes can be adapted to assess the effectiveness of data management systems in heterogeneous networks, process control systems, projects, logistics, etc.

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Section: Discussionmentioning

confidence: 99%

Evaluation of Unmanned-Aerial-Vehicle-Integrated Control System Efficiency on the Basis of Generalized Multiplicative Criterion

Vititin,

Kalyagin,

Kolesnichenko

2023

Inventions

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“…It plays an increasingly important role in disaster prevention and mitigation applications. After reviewing the currently available literature on landslide recognition in optical remote sensing images, we found that several classic models such as ResNet [24,25], YOLO [26][27][28][29][30], Mask R-CNN [31][32][33][34], U-Net [28,[35][36][37], DeeplabV3+ [38][39][40], Transformer [41][42][43], and EfficientNet [44,45] and several open landslide datasets such as Bijie landslide dataset [24], HR-GLDD dataset [46], CAS Landslide Dataset [47], and so on, have been popularly used for landslide recognition. In this paper, we will first introduce the fundamentals of landslide recognition based on deep learning and then discuss and analyze the current development status of each type of model; finally, we will compare the advantages and disadvantages of each model and analyze the development trends of landslide identification.…”

Section: Introductionmentioning

confidence: 99%

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

Cheng,

Wang,

Huang

et al. 2024

Remote Sensing

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Against the backdrop of global warming and increased rainfall, the hazards and potential risks of landslides are increasing. The rapid generation of a landslide inventory is of great significance for landslide disaster prevention and reduction. Deep learning has been widely applied in landslide identification due to its advantages in terms of its deeper model structure, high efficiency, and high accuracy. This article first provides an overview of deep learning technology and its basic principles, as well as the current status of landslide remote sensing databases. Then, classic landslide deep learning recognition models such as AlexNet, ResNet, YOLO, Mask R-CNN, U-Net, Transformer, EfficientNet, DeeplabV3+ and PSPNet were introduced, and the advantages and limitations of each model were extensively analyzed. Finally, the current constraints of deep learning in landslide identification were summarized, and the development direction of deep learning in landslide identification was analyzed. The purpose of this article is to promote the in-depth development of landslide identification research in order to provide academic references for the prevention and mitigation of landslide disasters and post-disaster rescue work. The research results indicate that deep learning methods have the characteristics of high efficiency and accuracy in automatic landslide recognition, and more attention should be paid to the development of emerging deep learning models in landslide recognition in the future.

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“…This two-step process is pivotal for pinpointing targets accurately in landslide identification. To detect landslides, Yun et al [45] introduced an optimized version of the Mask R-CNN model, which is predicated on the masked region. The model attains optimization through the incorporation of attention modules, utilization of bottom-up channels, and the introduction of GA-RPN.…”

Section: Introductionmentioning

confidence: 99%

Optimizing Geo-Hazard Response: LBE-YOLO’s Innovative Lightweight Framework for Enhanced Real-Time Landslide Detection and Risk Mitigation

Du,

Xu,

2024

Remote Sensing

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Prompt detection of landslides is crucial for reducing the disaster risk and preventing landslides. However, landslide detection in practical applications still faces many challenges, such as the complexity of environmental backgrounds, the diversity of target scales, and the enormity of model weights. To address these issues, this paper proposes a lightweight LBE-YOLO model for real-time landslide detection. Firstly, a lightweight model is designed by integrating the GhostConv lightweight network with the YOLOv8n model. Inspired by GhostConv, this study innovatively designed the GhostC2f structure, which leverages linear thinking to further reduce the model parameters and computational burden. Additionally, the newly designed EGC2f structure, incorporating an attention mechanism, not only maintains the model’s lightweight characteristics but also enhances the network’s capability to extract valid information. Subsequently, the Path Aggregation Network (PAN) was optimized by introducing a bidirectional feature propagation mechanism to improve the model’s feature fusion ability. Additionally, the Bijie landslide dataset was expanded through data augmentation strategies, thereby further improving the model’s generalization capability. The experimental results indicate that, compared to the YOLOv8n model, the proposed model increased accuracy by 4.2%, while the model’s weight and computational load were reduced by 32.0% and 35.5%, respectively. This verifies the superiority of the LBE-YOLO model in landslide target detection, which will help mitigate the impacts of natural disasters.

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Enhance the Accuracy of Landslide Detection in UAV Images Using an Improved Mask R-CNN Model: A Case Study of Sanming, China

Cited by 9 publications

References 52 publications

Evaluation of Unmanned-Aerial-Vehicle-Integrated Control System Efficiency on the Basis of Generalized Multiplicative Criterion

Evaluation of Unmanned-Aerial-Vehicle-Integrated Control System Efficiency on the Basis of Generalized Multiplicative Criterion

Advances in Deep Learning Recognition of Landslides Based on Remote Sensing Images

Optimizing Geo-Hazard Response: LBE-YOLO’s Innovative Lightweight Framework for Enhanced Real-Time Landslide Detection and Risk Mitigation

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