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An Improved encoder-decoder Network for Ore Image Segmentation

IEEE SENSORS JOURNAL 2021年第10期21卷 11469-11475页

作者： Yang, Hao Huang, Chao Wang, Long Luo, Xiong Univ Sci & Technol Beijing Shunde Grad Sch Foshan 528399 Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing 100083 Peoples R China Univ Sci & Technol Beijing Beijing Key Lab Knowledge Engn Mat Sci Beijing 100083 Peoples R China

Accurate segmentation of ore images plays a significant role in automatic geometric parameter detecting and composition analyzing of ore dressing progress. Semantic segmentation based on deep learning is a promising method for accurate ore image segmentation. However, the similar appearance with low contrast and blurry boundary of ores in image hamper segmentation accuracy. Moreover, it is difficult to train a deep network due to limited available ore images. In this work, an improved encoder-decoder network based on U-Net is proposed to handle above challenges. A contour awareness loss (CAL) is proposed to improve model sensitivity to misclassified pixels, pixels of similar appearance, and pixels near the boundary. The proposed scheme is verified on ore images by benchmarking against state-of-the-art segmentation methods. Experiment results show that the proposed scheme achieves 88.6% pixel-wise accuracy (PA) and 66.0% mean intersection over union (mIoU).

关键词： Image segmentation Task analysis Convolution Sensors Decoding Machine learning Data models Ore image multi-class segmentation encoder-decoder contour awareness loss

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A Bi-GRU-based encoder-decoder framework for multivariate time series forecasting

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SOFT COMPUTING 2024年第9-10期28卷 6775页

作者： Balti, Hanen Ben Abbes, Ali Farah, Imed Riadh Univ Manouba RIADI Lab ENSI Manouba 2010 Tunisia

Drought forecasting is crucial for minimizing the effects of drought, alerting people to its dangers, and assisting decision-makers in taking preventative action. This article suggests an encoder-decoder framework for multivariate times series (EDFMTS) forecasting. EDFMTS is composed of three layers: a temporal attention context layer, a gated recurrent unit (GRU)-based decoder component, and a bidirectional gated recurrent unit (Bi-GRU)-based encoder component. The proposed framework was evaluated usingmultivariate gathered from various sources in China (remote-sensing sensors, climate sensors, biophysical sensors, and so on). According to experimental results, the proposed framework outperformed the baselinemethods in univariate and multivariate times series (TS) forecasting. The correlation coefficient of determination (R-2), root-meansquared error (RMSE), and the mean absolute error (MAE) were used for the evaluation of the framework performance. The R-2, RMSE, and MAE are 0.94, 0.20, and 0.13, respectively, for EDFMTS. In contrast, the RMSE provided by autoregressive integrated moving average (ARIMA), PROPHET, long short-term memory (LSTM), GRU and convolutional neural network (CNN)-LSTM are 0.72, 0.92, 0.36, 0.40, and 0.27, respectively.

关键词： Deep learning Multivariate time series encoder-decoder Drought forecasting

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AdapNet: Adaptability Decomposing encoder-decoder Network for Weakly Supervised Action Recognition and Localization

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第4期34卷 1852-1863页

作者： Zhang, Xiao-Yu Li, Changsheng Shi, Haichao Zhu, Xiaobin Li, Peng Dong, Jing Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Beijing Inst Technol Beijing Peoples R China Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China China Univ Petr East China Coll Informat & Control Engn Qingdao Peoples R China Chinese Acad Sci Inst Automation Ctr Res Intelligent Percept & Comp Natl Lab Pattern Recognit Beijing Peoples R China

The point process is a solid framework to model sequential data, such as videos, by exploring the underlying relevance. As a challenging problem for high-level video understanding, weakly supervised action recognition and localization in untrimmed videos have attracted intensive research attention. Knowledge transfer by leveraging the publicly available trimmed videos as external guidance is a promising attempt to make up for the coarse-grained video-level annotation and improve the generalization performance. However, unconstrained knowledge transfer may bring about irrelevant noise and jeopardize the learning model. This article proposes a novel adaptability decomposing encoder-decoder network to transfer reliable knowledge between the trimmed and untrimmed videos for action recognition and localization by bidirectional point process modeling, given only video-level annotations. By decomposing the original features into the domain-adaptable and domain-specific ones based on their adaptability, trimmed-untrimmed knowledge transfer can be safely confined within a more coherent subspace. An encoder-decoder-based structure is carefully designed and jointly optimized to facilitate effective action classification and temporal localization. Extensive experiments are conducted on two benchmark data sets (i.e., THUMOS14 and ActivityNet1.3), and the experimental results clearly corroborate the efficacy of our method.

关键词： Videos Feature extraction Knowledge transfer Task analysis Adaptation models Data models Neural networks Action recognition encoder-decoder knowledge transfer point process temporal action localization

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An encoder-decoder ConvLSTM surrogate model for simulating geological CO2 sequestration with dynamic well controls

GAS SCIENCE AND ENGINEERING

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GAS SCIENCE AND ENGINEERING 2024年 125卷

作者： Feng, Zhao Tariq, Zeeshan Shen, Xianda Yan, Bicheng Tang, Xuhai Zhang, Fengshou Tongji Univ Coll Civil Engn Dept Geotech Engn Shanghai 200092 Peoples R China Tongji Univ Key Lab Geotech & Underground Engn Minist Educ Shanghai 200092 Peoples R China King Abdullah Univ Sci & Technol KAUST Phys Sci & Engn Div Thuwal 23955 Saudi Arabia Wuhan Univ Sch Civil Engn Wuhan 430072 Peoples R China Wuhan Univ Shenzhen Res Inst Shenzhen 518108 Peoples R China

In Geological Carbon Sequestration (GCS), effectively managing the project requires predicting state variables such as pressure and saturation. However, numerical simulation of multiphase flow in subsurface porous media involves solving large linear algebra systems, resulting in a substantial computational burden. This can make it impractical for real-time history matching or optimization. Meanwhile, deep learning -based surrogate models are emerging as fast and accurate approximators. This study delves into the application of the encoder -decoder Convolutional Long Short -Term Memory (ED-ConvLSTM) neural network for predicting the complex evolution of state variables under dynamic CO 2 injection schemes. Inception blocks enhanced with light -weighted attention modules are introduced in the encoder to extract high -dimensional input features. ConvLSTM is employed to propagate spatial temporal information in the low -dimensional latent space. Further, progressive upsampling blocks are used to reconstruct the latent features for the desired output. Instead of taking discrete time steps as an input feature, the proposed network captures the dynamic dependencies with the inherent ConvLSTM cell. The network has access to data at only portion of the initial time steps during training stage, while it is used to predict the state variables at unseen time steps during testing stage. Results show that the network can produce excellent predictions for both pressure and saturation, even at unseen future time steps. The remarkable generalizability to different geological permeability fields is also evaluated. ED-ConvLSTM outperforms the standard U -Net by far, especially when predicting beyond the training time period. These numerical experiments demonstrate the advantages of ED-ConvLSTM in terms of prediction accuracy, extrapolability and generalizability. This study highlights the importance of incorporating recurrent connections into the deep neural networks for simulating ti

关键词： Geological carbon sequestration Deep learning Surrogate model encoder-decoder Convolutional LSTM

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Context prior-based with residual learning for face detection: A deep convolutional encoder-decoder network

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SIGNAL PROCESSING-IMAGE COMMUNICATION 2020年 88卷 115948-115948页

作者： Zhou, Zexun He, Zhongshi Jia, Yuanyuan Du, Jinglong Wang, Lulu Chen, Ziyu Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Hanshan Normal Univ Coll Comp & Informat Engn Chaozhou 521041 Peoples R China Chongqing Med Univ Coll Med Informat Chongqing 400016 Peoples R China

In the field of security, faces are usually blurry, occluded, diverse pose and small in the image captured by an outdoor surveillance camera, which is affected by the external environment such as the camera pose and range, weather conditions, etc. It can be described as a problem of hard face detection in natural images. To solve this problem, we propose a deep convolutional neural network named feature hierarchy encoder-decoder network (FHEDN). It is motivated by two observations from contextual semantic information and the mechanism of multi-scale face detection. The proposed network is a scale-variant style architecture and single stage, which are composed of encoder and decoder subnetworks. Based on the assumption that contextual semantic information around face being auxiliary to detect faces, we introduce a residual mechanism to fuse context prior-based information into face feature and formulate the learning chain to train each encoder-decoder pair. In addition, we discuss some important factors in implement details such as the distribution of training dataset, the scale of feature hierarchy, and anchor box size, etc. They have some impact on the detection performance of the final network. Compared with some state-of-the-art algorithms, our method achieves promising performance on the popular benchmarks including AFW, PASCAL FACE, FDDB, and WIDER FACE. Consequently, the proposed approach can be efficiently implemented and routinely applied to detect faces with severe occlusion and arbitrary pose variations in unconstrained scenes. Our code and results are available on https://***/zzxcoder/EvaluationFHEDN.

关键词： Context prior Residual learning Deep convolutional neural network encoder-decoder Face detection

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Pedestrian Trajectory Prediction in Heterogeneous Traffic Using Pose Keypoints-Based Convolutional encoder-decoder Network

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 2021年第5期31卷 1764-1775页

作者： Chen, Kai Song, Xiao Ren, Xiaoxiang Beihang Univ Sch Automat Beijing 100191 Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China Nanan Primary Sch Taiyuan 032100 Shanxi Peoples R China

Future pedestrian trajectory prediction offers great prospects for many practical applications. Most existing methods focus on social interaction among pedestrians but ignore the factors that heterogeneous traffic objects (cars, dogs, bicycles, motorcycles, etc.) have significant influence on the future trajectory of a subject pedestrian. Also, the walking direction intention of a pedestrian may be referred by his/her pose keypoints. Considering this, this work proposes to predict a pedestrian's future trajectory by jointly using neighboring heterogeneous traffic information and his/her pose keypoints. To fulfill this, an end-to-end pose keypoints-based convolutional encoder-decoder network (PK-CEN) is designed, in which the heterogeneous traffic and pose keypoints are modeled as input. After training, PK-CEN is evaluated on manifold crowded video sequences collected from the public dataset MOT16, MOT17 and MOT20. Experimental results demonstrate that it outperforms state-of-the-art approaches, in terms of prediction errors.

关键词： Trajectory Tensors Predictive models Automobiles Computational modeling Visualization Legged locomotion Social-interaction pedestrian intention convolutional long-short-term memory encoder-decoder attention

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Land cover classification of synthetic aperture radar images based on encoder-decoder network with an attention mechanism

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JOURNAL OF APPLIED REMOTE SENSING 2022年第1期16卷

作者： Zheng, Nai-Rong Yang, Zi-An Shi, Xian-Zheng Zhou, Ruo-Yi Wang, Feng Fudan Univ Sch Informat Sci & Technol Key Lab Informat Sci Elect Waves Minist Educ Shanghai Peoples R China

More and more high-resolution synthetic aperture radar (SAR) image datasets have been available, which promote the applications of SAR images in land cover classification, such as vegetation monitoring, land cover, land use, cartography, etc. A novel algorithm based on the encoder-decoder structure is proposed in this paper. We add the channel attention and spatial attention modules to the algorithm for SAR images land cover classification. And these two modules, which interact with each other instead of being completely independent, can maximize the use of extracted information. Experiments on the high-resolution Gaofen-3 dataset have been carried out. Some classical semantic segmentation algorithms, including SegNet, HR-Net, Deeplabv3+, and GF3-baseline, are utilized to compare with the proposed algorithm. The experimental results demonstrate that the encoder-decoder network with the attention mechanism can get higher accuracy than other encoder-decoder networks. (C) 2022 Society of Photo-Optical Instrumentation Engineers (SPIE)

关键词： synthetic aperture radar Gaofen-3 land cover classification encoder-decoder attention mechanism

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Aircraft Trajectory Prediction With Enriched Intent Using encoder-decoder Architecture

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IEEE ACCESS 2022年 10卷 17881-17896页

作者： Tran, Phu N. Nguyen, Hoang Q., V Pham, Duc-Thinh Alam, Sameer Nanyang Technol Univ Sch Mech & Aerosp Engn Air Traff Management Res Inst Singapore 639798 Singapore

Aircraft trajectory prediction is a challenging problem in air traffic control, especially for conflict detection. Traditional trajectory predictors require a variety of inputs such as flight-plans, aircraft performance models, meteorological forecasts, etc. Many of these data are subjected to environmental uncertainties. Further, limited information about such inputs, especially the lack of aircraft tactical intent, makes trajectory prediction a challenging task. In this work, we propose a deep learning model that performs trajectory prediction by modeling and incorporating aircraft tactical intent. The proposed model adopts the encoder-decoder architecture and makes use of the convolutional layer as well as Gated Recurrent Units (GRUs). The proposed model does not require explicit information about aircraft performance and wind data. Results demonstrate that the provision of enriched aircraft intent, together with appropriate model design, could improve the prediction error up to 30% at a prediction horizon of 10 minutes (from 4.9 nautical miles to 3.4 nautical miles). The model also guarantees the mean error growth rate with increasing look-ahead time to be lower than 0.2 nautical miles per minute. In addition, the model offers a very low variance in the prediction, which satisfies the variance-standard specified by EUROCONTROL (EU Organization for Safety and Navigation of Air Traffic) for trajectory predictors. The proposed model also outperforms the state-of-the-art trajectory prediction model, where the Root Mean Square Error (RMSE) is reduced from 0.0203 to 0.0018 for latitude prediction, and from 0.0482 to 0.0021 for longitude prediction in a single prediction step of 15 seconds look-ahead. We showed that the pre-trained model on ADS-B data maintains its high performance, in terms of cross-track and along-track errors, when being validated in the Bluesky Air Traffic Simulator. The proposed model would significantly improve the performance of conflict detecti

关键词： Trajectory Aircraft Atmospheric modeling Predictive models Air traffic control Recurrent neural networks Uncertainty Aircraft trajectory prediction 4D trajectory machine learning encoder-decoder convolution neural network recurrent neural network

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encoder-decoder with Multi-scale Information Fusion for Semantic Image Segmentation 11

Encoder-Decoder with Multi-scale Information Fusion for Sema...

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11th International Conference on Graphics and Image Processing (ICGIP)

作者： Ma, Xinxin Liu, Kai Ding, Chongyang Yan, Lin Duan, Meiyu Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

ISBN: (纸本)9781510635241

Recently, semantic segmentation which requires to recovering all detailed information of the original image has achieved significant improvement. In this work, we utilize skip-layer, multi-scale context module, and encoder-decoder structure to perform the task of semantic image segmentation. To handle the problem that objects with different scales, we adopt the multi-scale context information module with different convolution filters to capture diverse range information in the encoder network. Furthermore, we utilize the skip-layer to fuse semantic information produced by a coarse layer and appearance information generated by a fine layer to recover more precise and detailed results. In order to prove the effect of the proposed model, we explain the implementation details. Finally, our model attains the test set performance of 71.8% mIoU on the PASCAL VOC 2012 dataset.

关键词： Semantic image segmentation multi-scale context module skip-layer encoder-decoder

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encoder-decoder Design for SAC-OCDMA using Flexible Cross Correlation (FCC) Code Algorithms

Encoder-Decoder Design for SAC-OCDMA using Flexible Cross Co...

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IEEE 4th International Conference on Photonics (ICP)

作者： Rashidi, C. B. M. Aljunid, S. A. Fadhil, Hilal A. Anuar, M. S. Univ Malaysia Perlis UniMAP Sch Comp & Commun Eng Kangar 01000 Perlis Malaysia

ISBN: (纸本)9781467360739;9781467360753

In this paper, we present an effective encoder-decoder design utilizing Flexible Cross Correlation (FCC) code for Spectral Amplitude Coding-Optical Code Division Multiple Access (SAC-OCDMA) systems. The FCC code has advantages, such as flexibility cross-correlation property for any given number of users and weights, as well as effectively reduces the impacts of Multiple-Access Interference (MAI). The proposed FCC SAC-OCDMA encoder-decoder had shown superior performance indicated FCC SAC-OCDMA encoder-decoder offers 100%, 287% and 331% much larger number of active users compared to MDW K=60, MFH K=31 and Hadamard K=29, respectively.

关键词： FCC Code encoder-decoder MAI SAC-OCDMA Systems

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