检索结果-内蒙古大学图书馆

Efficient encoder-decoder Network With Estimated Direction for SAR Ship Detection

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷

作者： Niu, Yuzhen Li, Yuezhou Huang, Jiangyi Chen, Yuzhong Fuzhou Univ Coll Comp & Data Sci Fujian Prov Key Lab Networking Comp & Intelligent Fuzhou 350116 Peoples R China Minist Educ Key Lab Spatial Data Min & Informat Sharing Fuzhou 350108 Fujian Peoples R China

Synthetic aperture radar (SAR) image ship detection has important applications in marine surveillance. There are two limitations when applying advanced detection methods naively for SAR ship detection. First, most detectors construct the model as an encoder and rely on the feature pyramid network (FPN) head for accurate prediction, which may lead to high computational costs. Second, the background noises in the ground truth (annotated as rectangular bounding boxes) of angular ships bring difficulties for model training. To meet these challenges, we propose an efficient encoder-decoder network with estimated direction for ship detection in SAR images. First, we present an anchor-free encoder-decoder model that can efficiently extract multiple-level features. Second, we formulate ship detection as a multitask learning problem, including a bounding box prediction and a ship direction regression. The estimated ship direction can weakly supervise and benefit ship detection. Furthermore, we develop a center-weighted labeling method for overlapped annotations. Comprehensive experiments on SAR-Ship-Detection and SSDD datasets show that our method achieves state-of-the-art performance with a high running speed.

关键词： Marine vehicles Radar polarimetry Synthetic aperture radar Decoding Feature extraction Task analysis Background noise encoder-decoder multitask learning ship detection in SAR image synthetic aperture radar (SAR) image

来源：评论

学校读者我要写书评

暂无评论

Automatic Crack Detection on Road Pavements Using encoder-decoder Architecture

引用

MATERIALS 2020年第13期13卷 2960页

作者： Fan, Zhun Li, Chong Chen, Ying Wei, Jiahong Loprencipe, Giuseppe Chen, Xiaopeng Di Mascio, Paola Shantou Univ Coll Engn Dept Elect & Informat Engn Key Lab Digital Signal & Image Proc Guangdong Pro Shantou 515063 Peoples R China Sapienza Univ Rome Dept Civil Construct & Environm Engn I-00184 Rome Italy Pusan Natl Univ Dept Ind Engn Busan 609735 South Korea

Automatic crack detection from images is an important task that is adopted to ensure road safety and durability for Portland cement concrete (PCC) and asphalt concrete (AC) pavement. Pavement failure depends on a number of causes including water intrusion, stress from heavy loads, and all the climate effects. Generally, cracks are the first distress that arises on road surfaces and proper monitoring and maintenance to prevent cracks from spreading or forming is important. Conventional algorithms to identify cracks on road pavements are extremely time-consuming and high cost. Many cracks show complicated topological structures, oil stains, poor continuity, and low contrast, which are difficult for defining crack features. Therefore, the automated crack detection algorithm is a key tool to improve the results. Inspired by the development of deep learning in computer vision and object detection, the proposed algorithm considers an encoder-decoder architecture with hierarchical feature learning and dilated convolution, named U-Hierarchical Dilated Network (U-HDN), to perform crack detection in an end-to-end method. Crack characteristics with multiple context information are automatically able to learn and perform end-to-end crack detection. Then, a multi-dilation module embedded in an encoder-decoder architecture is proposed. The crack features of multiple context sizes can be integrated into the multi-dilation module by dilation convolution with different dilatation rates, which can obtain much more cracks information. Finally, the hierarchical feature learning module is designed to obtain a multi-scale features from the high to low- level convolutional layers, which are integrated to predict pixel-wise crack detection. Some experiments on public crack databases using 118 images were performed and the results were compared with those obtained with other methods on the same images. The results show that the proposed U-HDN method achieves high performance because it can

关键词： pavement cracking automatic crack detection encoder-decoder deep learning U-net hierarchical feature dilated Convolution

来源：评论

学校读者我要写书评

暂无评论

Short-Term Load Forecasting Using encoder-decoder WaveNet: Application to the French Grid

引用

ENERGIES 2021年第9期14卷 2524页

作者： Dorado Rueda, Fernando Duran Suarez, Jaime del Real Torres, Alejandro IDENER Seville 41300 Spain Univ Seville Dept Syst & Automat Seville 41092 Spain

The prediction of time series data applied to the energy sector (prediction of renewable energy production, forecasting prosumers' consumption/generation, forecast of country-level consumption, etc.) has numerous useful applications. Nevertheless, the complexity and non-linear behaviour associated with such kind of energy systems hinder the development of accurate algorithms. In such a context, this paper investigates the use of a state-of-art deep learning architecture in order to perform precise load demand forecasting 24-h-ahead in the whole country of France using RTE data. To this end, the authors propose an encoder-decoder architecture inspired by WaveNet, a deep generative model initially designed by Google DeepMind for raw audio waveforms. WaveNet uses dilated causal convolutions and skip-connection to utilise long-term information. This kind of novel ML architecture presents different advantages regarding other statistical algorithms. On the one hand, the proposed deep learning model's training process can be parallelized in GPUs, which is an advantage in terms of training times compared to recurrent networks. On the other hand, the model prevents degradations problems (explosions and vanishing gradients) due to the residual connections. In addition, this model can learn from an input sequence to produce a forecast sequence in a one-shot manner. For comparison purposes, a comparative analysis between the most performing state-of-art deep learning models and traditional statistical approaches is presented: Autoregressive-Integrated Moving Average (ARIMA), Long-Short-Term-Memory, Gated-Recurrent-Unit (GRU), Multi-Layer Perceptron (MLP), causal 1D-Convolutional Neural Networks (1D-CNN) and ConvLSTM (encoder-decoder). The values of the evaluation indicators reveal that WaveNet exhibits superior performance in both forecasting accuracy and robustness.

关键词： time series forecasting energy consumption forecasting deep learning machine learning convolutional neural networks artificial neural networks causal convolutions dilated convolutions encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

Pooling Attention-based encoder-decoder Network for semantic segmentation

引用

COMPUTERS & ELECTRICAL ENGINEERING 2021年 93卷 107260-107260页

作者： Xu, Haixia Huang, Yunjia Hancock, Edwin R. Wang, Shuailong Xuan, Qijun Zhou, Wei Xiangtan Univ Coll Automat & Elect Informat Xiangtan 411105 Peoples R China Xiangtan Univ Coll Comp Sci Xiangtan 411105 Peoples R China Univ York Dept Comp Sci York YO10 5DD N Yorkshire England

Aiming to the challenge of poor pixel-consistency in inter-category and pixel-similarity in inter-category, in this paper, we propose an encoder-decoder network for image semantic segmentation using pooling SE-ResNet attention module, called PAEDN. It is an effective of attention mechanism to get aggregated information. According to the principle of SE-ResNet, a collection of Average, Maximum and Stochastic global pooling, which concentrate on contoured, detailed, and generalized information in a certain semantic segmentation, form attention modules. Channel Pooling Attention Module (CPAM) and Position Pooling Attention Module (PPAM) are designed and integrated into the encoder to extract discriminative features from input images, and the decoder is developed through SE-ResNet attention module to fuse the feature map in high-resolution with that in low-resolution. Experimental evaluations performed on the data sets PASCAL and Cityscapes, show the proposed encoder-decoder with pooling attention module produces good pixel-consistency semantic label, achieves 15.1% improvement to FCN.

关键词： Semantic segmentation encoder-decoder Pooling attention module Channel Position

来源：评论

学校读者我要写书评

暂无评论

An encoder-decoder deep learning method for multi-class object segmentation from 3D tunnel point clouds

引用

AUTOMATION IN CONSTRUCTION 2022年 137卷 104187-104187页

作者： Ji, Ankang Chew, Alvin Wei Ze Xue, Xiaolong Zhang, Limao Harbin Inst Technol Sch Management Harbin 150001 Peoples R China Bentley Syst Res Off 1 Harbourfront PlHarbourFront Tower One Singapore 098633 Singapore Guangzhou Univ Sch Management Guangzhou 510006 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn 1037 Luoyu Rd Wuhan 430074 Hubei Peoples R China

Discovering seepage is widely thought to be critical for maintaining the healthy conditions of the tunnel. Unfortunately, most of the seepage surveys are still manual with tedious, time-consuming, and inefficient as well as work-related physical injuries. To address this problem, this research proposes an encoder-decoder deep learning method combined with point cloud techniques for multi-class object segmentation, including seepage, from 3D tunnel point clouds. This method develops data processing and feature extraction techniques to perform normalization of 3D point clouds with full consideration of point features, followed by constructing voxels as input to the proposed encoder-decoder architecture for learning. In the training process, an optimal model is selected with a learning rate of 0.0001, a batch size of 256, and a voxel boundary of 8. Subsequently, the optimal well-trained model is applied to the testing set, achieving excellent performance. Comparisons with other stateof-the-art methods and four data processing strategies are conducted, demonstrating that the proposed method outperforms in segmenting large-scale 3D point clouds. Overall, the proposed method performs excellently, beneficially contributing to the multi-class object segmentation from 3D tunnel point clouds with great practical potential.

关键词： Deep learning encoder-decoder Multi-class object segmentation 3D tunnel point-clouds Segmentation

来源：评论

学校读者我要写书评

暂无评论

Effective improvement of multi-step-ahead flood forecasting accuracy through encoder-decoder with an exogenous input structure

引用

JOURNAL OF HYDROLOGY 2022年 609卷

作者： Cui, Zhen Zhou, Yanlai Guo, Shenglian Wang, Jun Xu, Chong-Yu Wuhan Univ State Key Lab Water Resources & Hydropower Engn S Wuhan 430072 Peoples R China Univ Oslo Dept Geosci POB 1047 Blindern N-0316 Oslo Norway

Accurate and reliable multi-step-ahead flood forecasting is beneficial for reservoir operation and water resources management. The encoder-decoder (ED) that can tackle sequence-to-sequence problems is suitable for multistep-ahead flood forecasting. This study proposes a novel ED with an exogenous input (EDE) structure for multi-step-ahead flood forecasting. The exogenous input can be the outputs of process-based hydrological models. This study constructs four multi-step-ahead flood forecasting approaches, including the Xinanjiang (XAJ) hydrological model, the single-output long short-term memory (LSTM) neural network with recursive strategies, the recursive ED combined with the LSTM neural network (LSTM-RED), and the LSTM-EDE models. The performance of these four models is evaluated and compared by the long-term 3 h hydrologic data series of the Lushui and Jianxi basins in China. The results show that the LSTM-RED model that integrates recursive strategies into the training process of neural networks is more advantageous than the LSTM model. The proposed LSTMEDE model can overcome the exposure bias problem, simplify its model structure, increase the computational efficiency in the validation process, and improve the multi-step-ahead flood forecasting accuracy, as compared to the LSTM-RED model.

关键词： Flood forecasting Multi-step-ahead Neural network Deep learning encoder-decoder Exogenous input

来源：评论

学校读者我要写书评

暂无评论

An encoder-decoder and modified U-Net network for microwave imaging of stroke

引用

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY 2024年第2期34卷 e22995-e22995页

作者： Liu, Jinzhen Chen, Liming Xiong, Hui Tiangong Univ Sch Control Sci & Engn Tianjin 300387 Peoples R China Tiangong Univ Tianjin Key Lab Intelligent Control Elect Equipmen Tianjin Peoples R China

Microwave imaging has been widely used in stroke diagnosis as a non-invasive, ionizing radiation-free imaging method. However, the electromagnetic inverse scattering problems of microwave imaging are nonlinear and ill-posed. To further improve the accuracy of microwave imaging algorithms to identify and reconstruct stroke regions. A novel encoder-decoder and Modified U-Net (ED-MUNET) network for microwave imaging of stroke is proposed. The newly proposed ED-MUNET method accomplishes the initial imaging from scattered data to stroke images through an encoder-decoder in the first step. In the second step, the feature information of the reconstructed image is extracted through the modified U-Net network, and the reconstruction from the rough stroke image to the high-resolution image is realized. The second step avoids black-box operations and improves image accuracy. ED-MUNET has fewer artifacts, the relative reconstruction error is less than 0.05, and the reconstructed images are clearer, according to the comparative experiments with other networks. The experimental results showed the superiority of the proposed method for reconstructing stroke images.

关键词： electromagnetic inverse scattering problems encoder-decoder microwave imaging stroke imaging U-net

来源：评论

学校读者我要写书评

暂无评论

IDM-Net: A Multi-Task Supported encoder-decoder Framework for Magnetic Field Inverse Design

引用

IEEE TRANSACTIONS ON APPLIED SUPERCONDUCTIVITY 2024年第8期34卷 1页

作者： Wang, Jiaqi Zhang, Qiankun Huazhong Univ Sci & Technol Sch Cyber Sci & Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol Hubei Engn Res Ctr Big Data Secur Sch Cyber Sci & Engn Hubei Key Lab Distributed Syst Secur Wuhan Peoples R China

We propose an end-to-end framework for inversely designing permanent magnets named IDM-Net. It utilizes a fundamental encoder-decoder architecture to handle multiple tasks. In more detail, the encoder is responsible for deeply extracting features of the magnetic field and categorizing different types of magnet shapes. The decoder focuses on inferring significant properties of each specific type of magnet shape, including size, position, and magnetization intensity. Such architecture breaks the critical limitation of designing only a single type of magnet in literature. Further, it allows for flexible choices of encoders' networks, such as convolutional neural networks (CNNs) or transformers, which are widely used in various computer vision tasks. Our experimental results demonstrate that the ResNet50-based and ViT-B/16-based IDM-Nets achieve accuracies of 93.8% and 91.4% in magnet shapes classification and errors of 0.31% and 0.33% in predicting magnetic properties, respectively.

关键词： Superconducting magnets Shape Magnetic resonance imaging Decoding Magnetic confinement Magnetic properties Magnetosphere Deep learning design optimization encoder-decoder inverse design magnetic field

来源：评论

学校读者我要写书评

暂无评论

CUFD: An encoder-decoder network for visible and infrared image fusion based on common and unique feature decomposition

引用

COMPUTER VISION AND IMAGE UNDERSTANDING 2022年第0期218卷

作者： Xu, Han Gong, Meiqi Tian, Xin Huang, Jun Ma, Jiayi Wuhan Univ Elect Informat Sch Wuhan 430072 Peoples R China

In this paper, we propose a novel method for visible and infrared image fusion by decomposing feature information, which is termed as CUFD. It adopts two pairs of encoder-decoder networks to implement feature map extraction and decomposition, respectively. On the one hand, the shallow features of the image contain abundant information while the deep features focus more on extracting the thermal targets. Thus, we use an encoder-decoder network to extract both shallow and deep features. Unlike existing methods, both of the shallow and deep features are used for fusion and reconstruction with different emphases. On the other hand, the infrared and visible features of the same layer have both similarities and differences. Therefore, we train the other encoder-decoder network to decompose the feature maps into common and unique information based on their similarities and differences. After that, we apply different fusion rules according to the flexible requirements. This operation is more beneficial to retain the significant feature information in the fusion results. Qualitative and quantitative experiments on publicly available TNO and RoadScene datasets demonstrate the superiority of our CUFD over the state-of-the-art.

关键词： Image fusion Infrared Visible Feature map encoder-decoder

来源：评论

学校读者我要写书评

暂无评论

CA-SegNet: A channel-attention encoder-decoder network for histopathological image segmentation

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2024年第PartA期96卷

作者： He, Feng Wang, Weibo Ren, Lijuan Zhao, Yixuan Liu, Zhengjun Zhu, Yuemin Harbin Inst Technol Inst Ultraprecis Optoelect Instrument Engn Harbin 150001 Peoples R China Harbin Inst Technol Key Lab Ultraprecis Intelligent Instrumentat Harbin 150001 Peoples R China Chengdu Univ Informat Technol Sch Software Engn Chengdu 610225 Peoples R China Harbin Inst Technol Res Ctr Adv Microscopy & Instrumentat Harbin 150001 Peoples R China Harbin Inst Technol Sch Phys Harbin 150001 Peoples R China Univ Lyon INSA Lyon CREATIS CNRSUMR 5220INSERMU1294 F-69621 Villeurbanne France

Histopathological image segmentation based on encoder-decoder architectures has emerged as a pivotal research area in medical image analysis. However, due to the irrelevant information within multi-channel representations from the encoder, the coarse reuse of shallow features in skip connections may burden the learning and even adversely affect the decoder. While various variants have been developed to cope with this issue, the performance remains unsatisfactory. In this work, we propose a novel encoder-decoder architecture named CA-SegNet to address the above issue more effectively and achieve advanced histopathological image segmentation. Our novelty is twofold: firstly, a bottleneck-structured decoder is developed to improve the integration of multi-channel feature representations, and secondly, a sequence of channel-attention feature fusion modules (CAFFMs) are developed to adaptively guide the reuse of fine-grained shallow features in skip connections while learning the channel-wise dependencies. Experimental results on different publicly available histopathological image datasets demonstrate that our CA-SegNet outperforms existing state-of-the-art methods on both large and small-scale datasets.

关键词： Deep learning Channel attention encoder-decoder Medical image segmentation Histopathological images

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：