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Proceedings of the 6th International Conference on Robotics and Artificial Intelligence

作者： Rong Zhang Rongguo Zhang Jiechao Ma Huiling Zhang Sun Yat-sen University China Infervision Medical Technology Co.Ltd. China Infervision Medical Technology Co. Ltd. China

ISBN: (纸本)9781450388597

Recently, CNNs (convolutional neural networks) have been widely used in the field of medical image segmentation. In particular, the encoder-decoder architectures represented by U-Net have achieved state-of-art segmentation effects and inspired many more elaborated networks, which adopt newer and more advanced network designs. To our knowledge, the comprehensive and detailed comparison among these improved versions from a multiplicity of points of view has not been conducted up to *** U-Net as the baseline, we select the other four typical improvements for U-Net. For higher reliability, we finish the task of segmentation on four datasets and more experiments are performed to test the performance in various conditions. Finally, we evaluate their performance using multiple evaluation *** find that attention U-Net achieves the best segmentation results in terms of F1-score but also owns the most trainable parameters and is most time-consuming. As training images decrease, the original U-Net is most robust even only less than 5 training samples are available. Besides, for any networks, adding auxiliary loss function with small weighting such as 0.01 or 0.01 whatever the cross-entropy loss and the dice-coefficient loss for the other one is beneficial as well.

关键词： Convolutional neural networks Segmentation encoder-decoder Biomedical image

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GPS Trajectory Completion Using End-to-End Bidirectional Convolutional Recurrent encoder-decoder Architecture with Attention Mechanism

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SENSORS 2020年第18期20卷 5143.-5143.页

作者： Nawaz, Asif Huang, Zhiqiu Wang, Senzhang Akbar, Azeem AlSalman, Hussain Gumaei, Abdu Nanjing Univ Aeronaut & Astronaut Dept Comp Sci & Technol Nanjing 210016 Peoples R China Nanjing Univ Aeronaut & Astronaut Key Lab Safety Crit Software Minist Ind & Informat Technol Nanjing 211106 Peoples R China Collaborat Innovat Ctr Novel Software Technol & I Nanjing 210093 Peoples R China King Saud Univ Dept Comp Sci Coll Comp & Informat Sci Riyadh 11543 Saudi Arabia

GPS datasets in the big data regime provide rich contextual information that enable efficient implementation of advanced features such as navigation, tracking, and security in urban computing systems. Understanding the hidden patterns in large amount of GPS data is critically important in ubiquitous computing. The quality of GPS data is the fundamental key problem to produce high quality results. In real world applications, certain GPS trajectories are sparse and incomplete;this increases the complexity of inference algorithms. Few of existing studies have tried to address this problem using complicated algorithms that are based on conventional heuristics;this requires extensive domain knowledge of underlying applications. Our contribution in this paper are two-fold. First, we proposed deep learning based bidirectional convolutional recurrent encoder-decoder architecture to generate the missing points of GPS trajectories over occupancy grid-map. Second, we interfaced attention mechanism between enconder and decoder, that further enhance the performance of our model. We have performed the experiments on widely used Microsoft geolife trajectory dataset, and perform the experiments over multiple level of grid resolutions and multiple lengths of missing GPS segments. Our proposed model achieved better results in terms of average displacement error as compared to the state-of-the-art benchmark methods.

关键词： GPS trajectory ConvLSTM encoder-decoder attention trajectory completion

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FFEDN: Feature Fusion encoder decoder Network for Crack Detection

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2022年第9期23卷 15546-15557页

作者： Liu, Chuanqi Zhu, Chengguang Xia, Xuan Zhao, Jiankang Long, Haihui Shanghai Jiao Tong Univ Dept Instrument Sci & Engn Shanghai 200240 Peoples R China Shenzhen Inst Artificial Intelligence & Robot Soc Shenzhen 518000 Peoples R China

Crack detection plays a crucial role in structural health monitoring tasks to ensure the reliability of the transportation infrastructures. However, the automatic detection of cracks remains a challenging task due to the complicated background. Especially, tiny crack detection should be attached importance because of its weak feature and background interference. Therefore, an end-to-end network Feature Fusion encoder decoder Network (FFEDN) with two novel modules is proposed to improve the crack detection accuracy. For one thing, the representation capability for tiny cracks is enhanced by introducing the attention mechanism, which redistributes and fuses different features of both the encoder and the decoder. For another, because high-level feature contains less interference, a shape semantic prior module is developed to learn the shape prior map that provides the rough shape and location information of cracks. This map is fed into the lower-level feature and helps it focus on crack areas, thereby suppressing background interference. To demonstrate the effectiveness of the proposed network, several experiments are implemented on three publicly available crack datasets. Compared with state-of-the-art crack detection methods, the novel network shows better performance on all the six evaluation metrics.

关键词： Feature extraction Decoding Shape Semantics Interference Task analysis Image edge detection Crack detection encoder-decoder feature fusion attention mechanism shape semantic prior

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Image Semantic Segmentation Based on encoder-decoder Network

Image Semantic Segmentation Based on Encoder-Decoder Network

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作者： Xiaopin Zhao Weibin Liu Weiwei Xing Institute of Information Science Beijing Jiaotong University School of Software Engineering Beijing Jiaotong University

Semantic segmentation is an extremely important task in computer vision. At present, the related methods have achieved high performance. Nevertheless, Semantic segmentation still faces the challenge of localization accuracy due to DCNN invariance and existence of objects at multi-scale. In order to improve the accuracy of segmentation, this paper proposes a U-SEM encoderdecoder network. Firstly, in the encoding stage, it down-samples through the ResNet. Secondly, in the decoding stage, in order to filter and utilize the useful features, the SE-Mobile Block is proposed and fused to the network. The SE block adopts the idea of attention mechanism to focus on useful features and ignore those redundant features. Mobile blocks use deep separable convolutions to replace traditional convolutions, speeding up operations and reducing parameters. Finally, it adopts the skip structure where the feature information of different scales are merged to produce accurate and detailed segmentation. Experimental results show that the proposed network achieves good performance on multiple datasets which reaches the accuracy of 78.4% m IOU on PASCAL VOC 2012 and 75.7% mIOU on Cityscapes dataset.

关键词： encoder-decoder Semantic segmentation Attention mechanism Mobile block

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Multi scale mirror connection based encoder decoder network for text localization

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PATTERN RECOGNITION LETTERS 2020年 135卷 64-71页

作者： Dutta, Kalpita Bal, Malyaban Basak, Arpita Ghosh, Swarnendu Das, Nibaran Kundu, Mahantapas Nasipuri, Mita Jadavpur Univ Dept CSE Kolkata 700032 WB India RCC Inst Technol Dept CSE Kolkata 700015 WB India

encoder decoder models with multi-scale feature concatenations have become ubiquitous for various natural scene segmentation tasks. In the current approach, a similar model with an improved mirror connection from encoders to decoder has been proposed. Three different types of mirror connections, namely, linear, parametric and convolutional, have been demonstrated in the proposed work. We have also implemented the use of internal skips to facilitate better gradient propagation within the encoder-decoder architecture. The proposed model also consists of an ensemble module that combines outputs from models with different kernel sizes, such as, 3 × 3, 5 × 5 and 7 × 7 to combine multi-scale features for efficient detections. The model was tested on the ICDAR 2003, SVT, ICDAR 2015 and the Total-Text dataset where it proved to be superior to other state of the art encoder-decoder architectures for pixel level classification.

关键词： Scene image Segmentation encoder-decoder Mirror skip

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Three-Dimensional Human Pose Estimation from Sparse IMUs through Temporal encoder and Regression decoder

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SENSORS 2023年第7期23卷 3547页

作者： Liao, Xianhua Dong, Jiayan Song, Kangkang Xiao, Jiangjian Ningbo Univ Sch Informat Sci & Engn Ningbo 315211 Peoples R China Chinese Acad Sci Ningbo Inst Mat Technol & Engn Ningbo 315201 Peoples R China

Three-dimensional (3D) pose estimation has been widely used in many three-dimensional human motion analysis applications, where inertia-based path estimation is gradually being adopted. Systems based on commercial inertial measurement units (IMUs) usually rely on dense and complex wearable sensors and time-consuming calibration, causing intrusions to the subject and hindering free body movement. The sparse IMUs-based method has drawn research attention recently. Existing sparse IMUs-based three-dimensional pose estimation methods use neural networks to obtain human poses from temporal feature information. However, these methods still suffer from issues, such as body shaking, body tilt, and movement ambiguity. This paper presents an approach to improve three-dimensional human pose estimation by fusing temporal and spatial features. Based on a multistage encoder-decoder network, a temporal convolutional encoder and human kinematics regression decoder were designed. The final three-dimensional pose was predicted from the temporal feature information and human kinematic feature information. Extensive experiments were conducted on two benchmark datasets for three-dimensional human pose estimation. Compared to state-of-the-art methods, the mean per joint position error was decreased by 13.6% and 19.4% on the total capture and DIP-IMU datasets, respectively. The quantitative comparison demonstrates that the proposed temporal information and human kinematic topology can improve pose accuracy.

关键词： three-dimensional human pose sparse IMUs encoder-decoder temporal convolutional encoder human kinematics hierarchy regression decoder

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A classification method based on encoder-decoder structure with paper content

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第9期34卷

作者： Yin, Yi Ouyang, Lin Wu, Zhixiang Yin, Shuifang Wuhan Univ Comp Sci & Technol Sch Comp Sci & Technol Wuhan Peoples R China Wuhan Univ Comp Sci & Technol Sch Sci Wuhan Peoples R China

The paper classification method aims to correctly divide the paper data according to the similarity of its content. However, how to accurately classify according to the content expressed in the paper has always been a problem that various classification algorithms need to face. At present, there is a kind of paper classification method based on deep learning and implemented by the encoder-decoder structure. This method inputs the words from a large number of papers into encoder, after calculating by NN (neural network) algorithm, the similarity degree of different papers is compared to achieve the purpose of classification. However, this type of method only considers the similarity between words, a NN algorithm can only calculate a large number of word information once, and it cannot find the regularity of classification through word information. But it has a difference with the similarity of the content. This paper starts from the perspective of considering the content, its label information is extracted, and the input vector of encoder-decoder structure is formed with labels and words. This improves the original paper classification method based on encoder-decoder structure. Firstly, the label information is based on the content, which can reflect the content of the paper. Secondly, the classification method which combines label information and word information can reflect the content of the paper comprehensively. Thirdly, the label information is independent of word information and NN algorithm is used separately to make this part of the content more consistent in the encoder-decoder structure. Finally, the label information and the word information are combined, respectively, with the output values obtained by different NN algorithms to realize the classification of the content. This paper proves the effectiveness of the proposed method by evaluating the paper data in web of science and obtaining relevant experimental results.

关键词： CNN encoder-decoder hidden information word embedding

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基于encoder-decoder网络的遥感影像道路提取

基于Encoder-Decoder网络的遥感影像道路提取

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作者：李代栋郑州大学

学位级别：硕士

随着遥感技术的飞速发展,高分辨率遥感影像改善了过去较低分辨率影像细节纹理和信息量不充足的问题,对于从遥感影像中提取道路区域的深入研究有极大的促进作用。高分辨率遥感影像中道路区域的提取和应用,对于现代智慧城市设计、智能交... 详细信息

随着遥感技术的飞速发展,高分辨率遥感影像改善了过去较低分辨率影像细节纹理和信息量不充足的问题,对于从遥感影像中提取道路区域的深入研究有极大的促进作用。高分辨率遥感影像中道路区域的提取和应用,对于现代智慧城市设计、智能交通道路网规划和实时路网检测系统等,在现实任务和场景中均具有较为广泛的应用价值和前景。然而在遥感影像中复杂的道路场景,往往导致提取结果中出现断裂、缺失等道路结构不完整,并且存在部分独立散点和斑块等情况。因此,如何利用海量的高分辨率遥感影像,自动、高效地从中提取出路网结构,并精确提取道路区域的边界结构,成为了一个研究的热点领域。本论文主要基于深度学习的相关算法,深入研究了针对高分辨率遥感影像道路区域的高质量提取任务,完成的主要工作如下:(1)针对提取高分辨率遥感影像中道路区域出现结构不完整、道路边界模糊的问题,设计并实现了由提取-优化双网络构成的EDRNet(EDNet)道路提取方法。首先,EDNet1道路提取网络的构建,利用了残差卷积模块保留道路区域信息,同时加速网络收敛,改善道路结构提取不完整的问题;其次,EDNet2道路优化网络,融合多尺度、多层次特征信息,对提取结果中道路边界进行优化改进;最后,根据各网络任务的特点,分别设计相应的损失函数对网络进行训练,充分发挥各网络的性能。(2)针对高分辨率遥感影像中道路区域呈带状分布的显著特征,在影像中狭长的道路区域提取困难的问题,设计并实现了SPUD-Res Net(SPUDNet)道路提取方法。首先,该方法由空洞卷积和残差模块构成编码器部分,并构建跳跃连接和相应的解码器部分,充分保留道路区域的细节信息;其次,在网络中构建条状池化模块和混合池化模块,增强对道路特征信息和道路区域间长距离依赖关系的获取能力;最后,对于网络训练所需的损失函数,利用结构相似性指数与道路的几何结构信息进行针对性设计,能够精细化提取的道路边界,去除网络提取结果之中较为模糊的预测部分。本文对基于encoder-decoder的语义分割网络进行了深入研究,并设计了提取遥感影像中的路网结构的深度学习语义分割方法。本文所提出的算法均能在一定程度上提升道路提取结果的准确性,对未来进行深度语义分割网络的深入研究及相关方法的应用都有十分重要的意义。

关键词：深度学习遥感影像道路提取 encoder-decoder ResNet

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Empirical autopsy of deep video captioning encoder-decoder architecture

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ARRAY 2021年 9卷

作者： Aafaq, Nayyer Akhtar, Naveed Liu, Wei Mian, Ajmal Univ Western Australia 35 Stirling Hwy Perth WA 6009 Australia

Contemporary deep learning based video captioning methods adopt encoder-decoder framework. In encoder, visual features are extracted with 2D/3D Convolutional Neural Networks (CNNs) and a transformed version of those features is passed to the decoder. The decoder uses word embeddings and a language model to map visual features to natural language captions. Due to its composite nature, the encoder-decoder pipeline provides the freedom of multiple choices for each of its components, e.g., the choices of CNN models, feature transformations, word embeddings, and language models etc. Component selection can have drastic effects on the overall video captioning performance. However, current literature is void of any systematic investigation in this regard. This article fills this gap by providing the first thorough empirical analysis of the role that each major component plays in a widely adopted video captioning pipeline. We perform extensive experiments by varying the constituent components of the video captioning framework, and quantify the performance gains that are possible by mere component selection. We use the popular MSVD dataset as the test-bed, and demonstrate that substantial performance gains are possible by careful selection of the constituent components without major changes to the pipeline itself. These results are expected to provide guiding principles for research in the fast growing direction of video captioning.

关键词： CNN architecture encoder-decoder Word embeddings Language model Video captioning Language and vision Video to text Natural language processing Recurrent neural networks

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Self-Attention based encoder-decoder for multistep human density prediction

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JOURNAL OF URBAN MOBILITY 2022年 2卷

作者： Violos, John Theodoropoulos, Theodoros Maroudis, Angelos-Christos Leivadeas, Aris Tserpes, Konstantinos Ecole Technol Super Dept Software & IT Engn 1100 Notre Dame St W Montreal PQ H3C 1K3 Canada Harokopio Univ Athens Dept Informat & Telemat 9 Omirou Tavros 16671 Greece

Multistep Human Density Prediction (MHDP) is an emerging challenge in urban mobility with lots of applications in several domains such as Smart Cities, Edge Computing and Epidemiology Modeling. The basic goal is to estimate the density of people gathered in a set of urban Regions of Interests (ROIs) or Points of Interests (POIs) in a forecast horizon of different granularities. Accordingly, this paper aims to contribute and go beyond the existing literature on human density prediction by proposing an innovative time series Deep Learning (DL) model and a geospatial feature preprocessing technique. Specifically, our research aim is to develop a highly-accurate MHDP model leveraging jointly the temporal and spatial components of mobility data. In the beginning, we compare 29 baseline and state-of-the-art methods grouped into six categories and we find that the statistical time series and Deep Learning encoders-decoders (ED) that we propose are highly accurate outperforming the other models based on a real and a synthetic mobility dataset. Our model achieves an average of 28.88 Mean Absolute Error (MAE) and 87.58 Root Mean Squared Error (RMSE) with 200,000 pedestrians per day distributed in multiple regions of interest in a 30 minutes time-window at different granularities. In addition, the geospatial feature transformation increases 4% further the RMSE of the proposed model compared to the state of the art solutions. Hence, this work provides an efficient and at the same time general applicable MHDP model that can benefit the planning and decision-making of many major urban mobility applications.

关键词： Mobility encoder-decoder Self attention Time series Deep learning Points of interest Regions of interest

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