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Multi-hazard susceptibility mapping based on Convolutional Neural Networks

Geoscience Frontiers 2022年第5期13卷 59-74页

作者： Kashif Ullah Yi Wang Zhice Fang Lizhe Wang Mahfuzur Rahman Institute of Geophysics and Geomatics China University of GeosciencesWuhan 430074China State Key Laboratory of Biogeology and Environmental Geology China University of GeosciencesWuhan 430074China School of Computer Science China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Geo-Information Processing China University of GeosciencesWuhan 430074China Department of Civil Engineering International University of Business Agriculture and Technology(IUBAT)Dhaka 1230Bangladesh

Multi-hazard susceptibility prediction is an important component of disasters risk management *** effective multi-hazard risk mitigation strategy includes assessing individual hazards as well as their ***,with the rapid development of artificial intelligence technology,multi-hazard susceptibility prediction techniques based on machine learning has encountered a huge *** order to effectively solve this problem,this study proposes a multi-hazard susceptibility mapping framework using the classical deep learning algorithm of Convolutional Neural Networks(CNN).First,we use historical flash flood,debris flow and landslide locations based on Google Earth images,extensive field surveys,topography,hydrology,and environmental data sets to train and validate the proposed CNN ***,the proposed CNN method is assessed in comparison to conventional logistic regression and k-nearest neighbor methods using several objective criteria,i.e.,coefficient of determination,overall accuracy,mean absolute error and the root mean square *** results show that the CNN method outperforms the conventional machine learning algorithms in predicting probability of flash floods,debris flows and ***,the susceptibility maps of the three hazards based on CNN are combined to create a multi-hazard susceptibility *** can be observed from the map that 62.43%of the study area are prone to hazards,while 37.57%of the study area are *** hazard-prone areas,16.14%,4.94%and 30.66%of the study area are susceptible to flash floods,debris flows and landslides,*** terms of concurrent hazards,0.28%,7.11%and 3.13%of the study area are susceptible to the joint occurrence of flash floods and debris flow,debris flow and landslides,and flash floods and landslides,respectively,whereas,0.18%of the study area is subject to all the three *** results of this study can benefit engineers,disaster managers and local government offici

关键词： Multi-hazard Convolutional Neural Network Machine learning Eastern Hindukush Pakistan

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LDD-YOLO: An Improved Lightweight Detection Method for Steel Surface Defects Based on YOLOv8

LDD-YOLO: An Improved Lightweight Detection Method for Steel...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yuechen Zhang Aimin Li Zhiyao Li Xiaotong Kong Wenqiang Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Steel is an indispensable raw material in the industrial field, steel surface defects seriously affect the quality of steel, in recent years a lot of research has been carried out on the detection of steel surface defects. Existing steel defect detection methods are unable to fully mine the underlying feature information of the target image and do not achieve a dynamic balance between accuracy and speed. To address the above problems, this paper proposes an optimised target detection algorithm based on YOLOv8. First, we proposed the DMCA module, which combines the ideas of deformable convolution and multi-channel self-attention mechanism. We developed a strengthen self-attention module to enhance the process of deformable convolutional generation of offsets, so that the model can better adapt to the complex shapes of different defective targets and extract features at a deeper level. Secondly, using the idea of LKA (Large Kernel Attention), we propose the LF-MSPP lightweight module with long-range dependence and adaptive capability to capture the tele-relationships with small computational cost and parameters, improved the problem of missing defective feature information. Finally, we replaced the head of the original YOLOv8 with a Dynamic Head and used the split attention mechanism to improve the head detection capabilities while ensuring lightweight. We conduct extensive experiments on the widely used Northeastern University steel defect dataset NEU-DET. Experimental results show that the improved model improves mAP@50, mAP@50-95, AP and AR indicators by 2.4%, 2.0%, 6.1%, and 3.4% respectively compared with the original YOLOv8 model, and the number of model parameters is reduced by 11.2%. The improved model is also better than mainstream defect target detection models such as SSD, Retinanet, FasterRCNN, YOLOv5, YOLOv6, YOLOv7, YOLOv8, etc, and can better meet the accuracy and speed requirements of actual industrial production for steel surface defect detection mode

关键词： YOLO Accuracy Head Three-dimensional displays Shape Convolution Feature extraction Steel Kernel Defect detection

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MF-YOLO: Multimodal Fusion for Remote Sensing Object Detection Based on YOLOv5s

MF-YOLO: Multimodal Fusion for Remote Sensing Object Detecti...

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International Conference on computer Supported Cooperative Work in Design

作者： Wenqiang Li Aimin Li Xiaotong Kong Yuechen Zhang Zhiyao Li Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Remote sensing object detection has flourished at a rapid rate of development. In remote sensing images (RSI), most algorithms perform well in detecting medium and large objects but perform poorly on small objects. This is because small objects occupy fewer pixels in the entire image, and the detector is more likely to be biased towards detecting large objects. In addition, the huge background in the image also introduces noise, causing many detection failures. Most approaches solve the problem of small object detection by increasing the depth of the neural network, but the results are not satisfactory. To solve the above problems, we propose a new method, MF-YOLO, that utilizes fused infrared (IR) and red-green-blue (RGB) images for remote sensing object detection. Through multimodal fusion (MF), we can obtain more positive information, thereby improving detection accuracy. In addition, we introduced the Bi-Level Routing Attention (BRA) module to improve the YOLOv5s model structure, and proposed a new loss function to enhance the model’s learning discrimination ability. Experimental results show that the accuracy of MF-YOLO on the VEDAI and NWPU VHR-10 datasets is 76.62% and 91.63% respectively (in terms of mAP @0.5 ). Compared with the existing state-of-the-art, the detection accuracy is significantly improved. In addition, our model has fewer weight parameters than YOLOv5s, allowing it to achieve relatively high near-real-time detection speed.

关键词： YOLO Accuracy Federated learning Noise Neural networks Prediction algorithms Routing

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Predicting Token Impact Towards Efficient Vision Transformer

arXiv

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arXiv 2023年

作者： Wang, Hong Yang, Su Huang, Xiaoke Zhang, Weishan Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China School of Computer Science and Technology China University of Petroleum China

Token filtering to reduce irrelevant tokens prior to self-attention is a straightforward way to enable efficient vision Transformer. This is the first work to view token filtering from a feature selection perspective, where we weigh the importance of a token according to how much it can change the loss once masked. If the loss changes greatly after masking a token of interest, it means that such a token has a significant impact on the final decision and is thus relevant. Otherwise, the token is less important for the final decision, so it can be filtered out. After applying the token filtering module generalized from the whole training data, the token number fed to the self-attention module can be obviously reduced in the inference phase, leading to much fewer computations in all the subsequent self-attention layers. The token filter can be realized using a very simple network, where we utilize multi-layer perceptron. Except for the uniqueness of performing token filtering only once from the very beginning prior to self-attention, the other core feature making our method different from the other token filters lies in the predictability of token impact from a feature selection point of view. The experiments show that the proposed method provides an efficient way to approach a light weighted model after optimized with a backbone by means of fine tune, which is easy to be deployed in comparison with the existing methods based on training from scratch. © 2023, CC BY.

关键词： Feature extraction

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SIEDOB: Semantic Image Editing by Disentangling Object and Background

arXiv

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arXiv 2023年

作者： Luo, Wuyang Yang, Su Zhang, Xinjian Zhang, Weishan Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University China School of Computer Science and Technology China University of Petroleum China

Semantic image editing provides users with a flexible tool to modify a given image guided by a corresponding segmentation map. In this task, the features of the foreground objects and the backgrounds are quite different. However, all previous methods handle backgrounds and objects as a whole using a monolithic model. Consequently, they remain limited in processing content-rich images and suffer from generating unrealistic objects and texture-inconsistent backgrounds. To address this issue, we propose a novel paradigm, Semantic Image Editing by Disentangling Object and Background (SIEDOB), the core idea of which is to explicitly leverages several heterogeneous subnetworks for objects and backgrounds. First, SIEDOB disassembles the edited input into background regions and instance-level objects. Then, we feed them into the dedicated generators. Finally, all synthesized parts are embedded in their original locations and utilize a fusion network to obtain a harmonized result. Moreover, to produce high-quality edited images, we propose some innovative designs, including Semantic-Aware Self-Propagation Module, Boundary-Anchored Patch Discriminator, and Style-Diversity Object Generator, and integrate them into SIEDOB. We conduct extensive experiments on Cityscapes and ADE20K-Room datasets and exhibit that our method remarkably outperforms the baselines, especially in synthesizing realistic and diverse objects and texture-consistent backgrounds. Code is available at https://***/WuyangLuo/SIEDOB. Copyright © 2023, The Authors. All rights reserved.

关键词： Textures

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Regression-Guided Positive Sample Refocusing Paradigm for Tiny Object Detection in Aerial Images

Regression-Guided Positive Sample Refocusing Paradigm for Ti...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Lihui Ge He Chen Guanqun Wang Tong Zhang Yin Zhuang Fukun Bi Liang Chen National Key Laboratory of Science and Technology on Space-Born Intelligent Information Processing Beijing Institute of Technology Beijing China School of Computer Science Peking University Beijing China College of Information North China University of Technology Beijing China

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

Tiny object detection represents a pivotal challenge in remote sensing intelligent interpretation, necessitating detectors to exhibit heightened precision in object localization. However, typical model optimization strategies cannot release the detector’s potential for precisely localizing objects. And the lack of interpretability in detection box filtering based on object classification scores serves as a constraint on further performance improvement. Therefore, this paper proposed a novel model optimization strategy to thoroughly unleash the potential of the detector for precise localization. Then, the utilization of object comprehensive confidence score enhances the interpretability of the post-processing step for detection boxes. Rigorous experiments on the AI-TOD dataset have demonstrated the effectiveness of our method, achieving state-of-the-art performance.

关键词： Location awareness Filtering Detectors Object detection Optimization Remote sensing

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Tomato Leaf Disease Identification and Growth Dataset Augmentation Based on UAE-GAN

Tomato Leaf Disease Identification and Growth Dataset Augmen...

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International Conference on Intelligent Control and information processing (ICICIP)

作者： Xin Li Fang Li Xiaonan Luo School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China Center for Applied Mathematics of Guangxi (GUET) Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin China Guangxi Key Laboratory of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin China

ISBN: (数字)9798331516147

ISBN: (纸本)9798331516154

To delve into the characterization of growth disorders in different crops, it is important to support the model with a large amount of image data that includes a variety of disease types and disease levels to capture the typical and subtle differences of various diseases on plant leaves. However, the actual process of gathering data is challenging, sample coverage is challenging to accomplish, data capture is impeded, and the quality of the data is subpar. This work aims to address the issue of data shortages by employing technical methods. In particular, we creatively investigated the UAE-GAN approach, which naturally combines CycleGAN, U-Net, Variational Autoencoder VAE, and Autoencoder to increase the data. Among these, U-Net can precisely extract the small details of disease locations in crop photos and provide a strong basis for further processing thanks to its special codec architectural benefits. The Variational Autoencoder (VAE) significantly enhances the diversity of data by mapping the image to the latent space and sampling based on a certain probability distribution, so producing new image samples that are distinct from the original image yet inherently connected. Learning the coding and decoding of the original image is the foundation of autoencoders. If a mild disruption is introduced into the coding process, it can achieve data augmentation in another dimension and create a sequence of new images with just little modifications to the original image. The aforementioned models are closely linked with CycleGAN to efficiently map and convert in a variety of picture domains and to fully leverage CycleGAN's remarkable unsupervised image conversion capabilities. The perception ability, feature capture ability, and information conversion ability of the fusion model for crop image data are significantly improved, and the key elements of each link in the data enhancement process are comprehensively considered to ensure that the generated new image data can not o

关键词： Training Image coding Autoencoders Data enhancement Crops Generators Data models Stability analysis Probability distribution Diseases

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Topic Embedded Representation Enhanced Variational Wasserstein Autoencoder for Text Modeling 5

Topic Embedded Representation Enhanced Variational Wasserste...

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5th IEEE International Conference on Electronics technology, ICET 2022

作者： Xiang, Zheng Liu, Xiaoming Yang, Guan Liu, Yang Zhongyuan University of Technology School of Computer Science Zhengzhou China Henan Key Laboratory on Public Opinion Intelligent Analysis Zhengzhou China Key Laboratory of text processing and image understanding Zhengzhou China Xidian University State Key Laboratory of Integrated Services Networks Xi'an China Shandong University Key Lab of Cryptologic Technology and Information Security Ministry of Education Xi'an China

ISBN: (纸本)9781665485081

Variational Autoencoder (VAE) is now popular in modeling and language generation tasks, which need to pay attention to the diversity of generation results. The existing models are insufficient in capturing the built-in relationships between topic representation and sequential words. At the same time, there is a massive contradiction between the commonly used simple Gaussian prior and the actual complex distribution of languages. To address the above problems, we introduce a hybrid Wasserstein Autoencoder (WAE) with Topic Embedded Representation (TER) for modeling. TER is obtained through an embedding-based topic model and can capture the dependencies and semantic similarities between topics and words. In this case, the learned latent variable has rich semantic knowledge with the help of TER and is easier to explain and control. Our experiments show that our method is competitive with other VAEs in modeling. © 2022 IEEE.

关键词： Semantics

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Radar HRRP Open Set Recognition Using Hierarchical Prototype Learning

Radar HRRP Open Set Recognition Using Hierarchical Prototype...

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2024 International Radar Conference, RADAR 2024

作者： Liu, Yichen Wang, Yanhua Zhang, Liang Yu, Binhua Yang, Xiaojing Zhang, Yu-Ang Zheng, Le Zou, Difan Beijing Institute of Technology School of Information and Electronics Beijing 100081 China Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing 100081 China University of Hong Kong Department of Computer Science Hongkong Hong Kong University of Hong Kong HKU Musketeers Foundation Institute of Data Science Hongkong Hong Kong

ISBN: (纸本)9798350362381

High-resolution range profiles (HRRP) are increasingly employed in radar automatic target recognition (RATR). In practical applications of RATR, the environment is open and dynamic, and the recognition model is likely to encounter targets of unseen categories. However, traditional methods focus on a close-set scenario, where all categories in the test set are known during training. To bridge this gap, this paper proposes an open-set recognition (OSR) method based on hierarchical classification, developed from prototype learning. This method improves the generalization performance for recognizing known categories and identifies unknown categories while providing information about how the unknown categories relate to the known ones. Specifically, first, a four-layer category hierarchy is built based on prior knowledge to guide the training and testing stages. Next, we propose a hierarchical prototype loss (HPL) to constrain the feature space extracted by the prototype network so that the feature distribution of objects is consistent with the hierarchical category structure. Lastly, the trained prototype network makes predictions following the hierarchical structure. This operation could provide relationship information between the unknown and known categories. Extensive experiments on measured HRRP data validate the effectiveness of our proposed method for open-set recognition. © 2024 IEEE.

关键词： hierarchical classification open set recognition prototype learning radar target recognition

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Label Distribution Learning with Correlation information

SSRN

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SSRN 2024年

作者： Wu, Yilin Lin, Yaojin Guo, Wenzhong Ding, Weiping College of Computer and Data Science Fuzhou University Fuzhou350116 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou350116 China Key Laboratory of Data Science and Intelligence Application Minnan Normal University Zhangzhou363000 China School of Information Science and Technology Nantong University Nantong226019 China

Label distribution learning quantifies the label space for each instance and has widely applicability in different fields. However, most existing works primarily focus on label correlation, but they still have a limitation in capturing instance correlation. Meanwhile, traditional label distribution learning operates on individual labels sequentially, leading to limitation in applying common features. Therefore, in this paper, we propose a novel approach for label distribution learning with correlation information i.e. instance correlation and label correlation. Specifically, the instance correlation and label correlation are identified by an optimization function and Pearson correlation coefficient, respectively. Besides, we conduct $\ell_{2,1}-$norm regularization to exploit common features. Comprehensive experiments manifest the superiority of our proposed approach against other well-established label distribution learning algorithms. © 2024, The Authors. All rights reserved.

关键词： Learning algorithms

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