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IEEE OPEN JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS 2022年 3卷 126-136页

作者： Wang, Hu Chen, Hao Wu, Qi Ma, Congbo Li, Yidong Univ Adelaide Sch Comp Sci Adelaide SA 5005 Australia Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing 100044 Peoples R China

The control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. However, it is challenging since traffic dynamics are complicated in real-world scenarios. Because of the high complexity of the optimisation problem for modeling the traffic, experimental settings of existing works are often inconsistent. Moreover, it is not trivial to control multiple intersections properly in real complex traffic scenarios due to its vast state and action space. Failing to take intersection topology relations into account also results in inferior solutions. To address these issues, in this work we carefully design our settings and propose a new dataset including both synthetic and real traffic data in more complex scenarios. Additionally, we propose a novel baseline model with strong performance. It is based on deep reinforcement learning with an encoder-decoder structure: an edge-weighted graph convolutional encoder to excavate multi-intersection relations;and an unified structure decoder to jointly model multiple junctions in a comprehensive manner, which significantly reduces the number of the model parameters. By doing so, the proposed model is able to effectively deal with the multi-intersection traffic control optimisation problem. Models are trained/tested on both synthetic and real maps and traffic data with the Simulation of Urban Mobility (SUMO) simulator. Experimental results show that the proposed model surpasses multiple competitive methods. The traffic data and the code can be found at https://***/JPdU1.

关键词： Optimization Junctions Switches Reinforcement learning Convolution Markov processes Urban areas Traffic control optimisation multiple intersections deep reinforcement learning encoder-decoder structure

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A multi view scene modeling method for indoor landscape considering adjacent region fusion

A multi view scene modeling method for indoor landscape cons...

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2024 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2024

作者： Li, Minyong Xiamen Xingcai Vocational & Technical College Fujian Xiamen 361004 China

ISBN: (纸本)9781510689275

To improve the accuracy of indoor digital landscape scene modeling and enhance its overall visual effect, this paper proposes a multi view indoor landscape scene modeling method that considers adjacent area fusion. Firstly, utilizing a deep superpoint graph network model to achieve indoor landscape segmentation;Next, fine processing is performed through an encoder decoder. Afterwards, collect multi view 3D sampling points and integrate the information using multi frame fusion technology. To optimize the fusion effect, the adjacent region convex fusion strategy is adopted to ensure accurate and consistent information. Finally, with the help of SfM algorithm, high-precision multi view 3D indoor landscape modeling was achieved. The experimental results show that this method has high modeling accuracy and good application effects. © 2025 SPIE.

关键词： 3D scene digital landscape encoder-decoder structure neighborhood convex fusion SfM algorithm

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A Hybrid Inversion Method Based on SDM and ANNs Considering Electromagnetic Response Laws

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Lang, Shinan Huang, Wenbin Huang, Ling Liu, Xiaojun Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Chinese Acad Sci Inst Elect Key Lab Electromagnet Radiat & Sensing Technol Beijing 100094 Peoples R China Chinese Acad Sci Aerosp Informat Res Inst AIRCAS Beijing 100094 Peoples R China

Among the inversion methods for airborne transient electromagnetic (ATEM) data, the hybrid inversion method integrates the iterative optimization framework with artificial neural networks (ANNs), ensuring inversion accuracy while enhancing the generalization capability of neural networks. However, this method faces challenges in terms of slow computation speeds due to its lower updated step length and the lack of consideration for electromagnetic response laws. Our method adopts a supervised descent method (SDM) framework to supervise the ANNs, obtaining a longer updated step length. On the basis of the SDM framework, we have considered the electromagnetic response laws and designed RNN-ResNet and 1-D-UNet networks to update the conductivity model, improving the computing speed. Through numerical ablation experiments, we validated the effectiveness of our proposed method and compared the inversion results with those obtained using the traditional hybrid method. Additionally, we conducted tests on bundle fringe distribution, inversion fitting loss, noise sensitivity, and inversion speed for both methods using measured data to evaluate their performance in practical applications. The experimental findings demonstrate that our method achieves the same level of inversion accuracy and generalization ability as the traditional hybrid method while enhancing inversion speed by up to 68.5%.

关键词： Conductivity Electromagnetics Accuracy Iterative methods Optimization Data models Atmospheric modeling Airborne transient electromagnetic (ATEM) data inversion controlled-source electromagnetics (CSEM) encoder-decoder structure rapid inversion method supervised descent method (SDM)

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DAEA-Net: Dual Attention and Elevation-Aware Networks for Airborne LiDAR Point Cloud Semantic Segmentation

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2024年 62卷 1页

作者： Zhu, Yurong Liu, Zhihui Liu, Changhong China Univ Geosci Sch Math & Phys Wuhan 430074 Peoples R China China Univ Geosci Hubei Key Lab Intelligent Geoinformat Proc Wuhan 430078 Peoples R China

Semantic segmentation of airborne laser scanning (ALS) point clouds remains a challenging task due to the complexity and diversity of 3-D scenes in the real world. Currently, most deep learning-based airborne LiDAR point cloud segmentation methods prioritize designing local feature extraction operators while overlooking the long-range dependencies among neighborhoods and the inherently diverse properties of point cloud data. To address these issues, this article introduces a dual-attention and elevation-aware airborne LiDAR point cloud semantic segmentation network (DAEA-Net) built upon an encoding-decoding architecture. First, we develop a cross multiple anti-affine attention (CMAAA) module that effectively captures global contextual information across different neighborhoods through interactive learning of multiple features. Second, we introduce an elevation awareness (EA) module that uses normal vectors to establish a geometric similarity discriminant for each neighboring point. It incorporates an autoencoder architecture to fuse elevation information, enhancing the horizontal structural dissimilarity between objects of similar height while enriching the representation of elevation data. Additionally, to compensate for the potential information loss in the encoding-decoding hierarchical structure, we design a lightweight U-global attention (UGA) module to link decoding and encoding hierarchical levels. It merges features of different resolutions and levels during downsampling and upsampling through pooling while utilizing the self-attention mechanism to enhance the network's global expression capability. The proposed DAEA-Net enhances ALS semantic segmentation performance by enabling interactive learning of multiple features and effectively representing elevation information. Extensive experiments conducted on two datasets demonstrate that our method delivers superior semantic segmentation performance compared to several existing advanced techniques.

关键词： Point cloud compression Feature extraction Semantic segmentation Three-dimensional displays Laser radar Geology Encoding Attention mechanism elevation information encoder-decoder structure global perception point cloud semantic segmentation

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Enhanced pixel-level crack detection using Att-SegCrack: an improved CNN with feature fusion and large receptive field

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ROAD MATERIALS AND PAVEMENT DESIGN 2025年

作者： Su, Liangliang Huang, Hao Yang, Yalong Liu, Yunlin Niu, Zhen Hu, Qizhi Anhui Jianzhu Univ Anhui Prov Key Lab Intelligent Bldg & Bldg Energy Hefei Peoples R China Anhui Jianzhu Univ Anhui Inst Strateg Study Carbon Dioxide Emiss Peak Hefei Peoples R China Anhui Jianzhu Univ Sch Elect & Informat Engn Hefei Peoples R China Anhui Jianzhu Univ Coll Civil Engn Hefei Peoples R China

Deep convolutional neural networks have demonstrated significant advancements in pavement crack detection. Nevertheless, challenges persist in achieving satisfactory performance due to discontinuous crack edges and low background contrast. This paper presents Att-SegCrack, an enhanced encoder-decoder network that addresses these limitations through three key components. First, a simple yet effective feature fusion scheme restores crack details by bilinearly up-sampling encoder features and integrating them with outputs from the penultimate decoder layer, which subsequently serves as input to the final decoding layer. Second, dilated convolutions expand the receptive field to capture comprehensive contextual information for complete crack profiles. Third, the convolutional block attention module enhances crack-background differentiation in low-level features. Evaluations on two benchmark datasets (Crack500 and DeepCrack) demonstrate that our method outperforms other state-of-the-art methods in crack detection performance.

关键词： Crack detection multi-scale feature fusion attention mechanism dilated convolution encoder-decoder structure

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Ship Segmentation via encoder-decoder Network With Global Attention in High-Resolution SAR Images

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2022年 19卷 1页

作者： Li, Jichao Gou, Shuiping Li, Ruimin Chen, Jia-Wei Sun, Xiaolong Xidian Univ Key Lab Intelligent Percept & Image Understanding Minist Educ Xian 710071 Peoples R China Xidian Univ Acad Adv Interdisciplinary Res Xian 710071 Shaanxi Peoples R China China Elect Technol Grp Corp 20th Res Inst Xian 710068 Shaanxi Peoples R China

Ship detection in the synthetic aperture radar (SAR) image is of great significance in the fields of military and coastal defense. Most ship detection methods are designed based on the object detection framework, which can only provide the vertices' coordinates of the bounding box covering the ship targets but cannot provide more detailed contour information. Target segmentation can further explore the shape and edge information of the objects, which can be used as a blazing novel means for automatic object detection. In this letter, a 3-D atrous encoder-decoder neural network with global attention modules (GAM-EDNet) is proposed to achieve ship segmentation in SAR images. The encoder-decoder structure with atrous convolution is developed as the network body to fully exploit the structural information of the ship targets with various sizes. To increase the structural information of the single-polarization SAR images, a 3-D image cube is designed as the input of the GAM-EDNet. A global attention module is proposed to further improve the segmentation performance by integrating the high-level semantic features with the low-level location features. Besides, an SAR ship segmentation dataset (SAR-HR4) is built to evaluate the segmentation performance, and the experimental results show that the proposed GAM-EDNet achieves better performance than other state-of-the-art methods.

关键词： Marine vehicles Image segmentation Radar polarimetry Semantics Synthetic aperture radar Feature extraction Wavelet transforms encoder-decoder structure global attention module high-resolution synthetic aperture radar (SAR) images ship segmentation

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Design of oral English teaching model based on multi-modal perception of the Internet of Things and improved conventional neural networks

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PEERJ COMPUTER SCIENCE 2023年 9卷 e1503页

作者： Qin, Haitao Hubei Normal Univ Coll Foreign Studies Huangshi Hubei Peoples R China

Oral English instruction plays a pivotal role in educational endeavors. The emergence of online teaching in response to the epidemic has created an urgent demand for a methodology to evaluate and monitor oral English instruction. In the post-epidemic era, distance learning has become indispensable for educational pursuits. Given the distinct teaching modality and approach of oral English instruction, it is imperative to explore an intelligent scoring technique that can effectively oversee the content of English teaching. With this objective in mind, we have devised a scoring approach for oral English instruction based on multi-modal perception utilizing the Internet of Things (IoT). Initially, a trained convolutional neural network (CNN) model is employed to extract and quantify visual information and audio features from the IoT, reducing them to a fixed dimension. Subsequently, an external attention model is proposed to compute spoken English and image characteristics. Lastly, the content of English instruction is classified and graded based on the quantitative attributes of oral dialogue. Our findings illustrate that our scoring model for oral English instruction surpasses others, achieving the highest rankings and an accuracy of 88.8%, outperforming others by more than 2%.

关键词： Oral English teaching Multi-modal perception encoder-decoder structure CNN

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Rethinking Transformers for Semantic Segmentation of Remote Sensing Images

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2023年 61卷 1页

作者： Liu, Yuheng Zhang, Yifan Wang, Ye Mei, Shaohui Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Peoples R China

Transformer has been widely applied in image processing tasks as a substitute for convolutional neural networks (CNNs) for feature extraction due to its superiority in global context modeling and flexibility in model generalization. However, the existing transformer-based methods for semantic segmentation of remote sensing (RS) images are still with several limitations, which can be summarized into two main aspects: 1) the transformer encoder is generally combined with CNN-based decoder, leading to inconsistency in feature representations;and 2) the strategies for global and local context information utilization are not sufficiently effective. Therefore, in this article, a global-local transformer segmentor (GLOTS) framework is proposed for the semantic segmentation of RS images to acquire consistent feature representations by adopting transformers for both encoding and decoding, in which a masked image modeling (MIM) pretrained transformer encoder is adopted to learn semantic-rich representations of input images and a multiscale global-local transformer decoder is designed to fully exploit the global and local features. Specifically, the transformer decoder uses a feature separation-aggregation module (FSAM) to utilize the feature adequately at different scales and adopts a global-local attention module (GLAM) containing global attention block (GAB) and local attention block (LAB) to capture the global and local context information, respectively. Furthermore, a learnable progressive upsampling strategy (LPUS) is proposed to restore the resolution progressively, which can flexibly recover the fine-grained details in the upsampling process. The experiment results on the three benchmark RS datasets demonstrate that the proposed GLOTS is capable of achieving better performance with some state-of-the-art methods, and the superiority of the proposed framework is also verified by ablation studies. The code will be available at https://***/lyhnsn/GLOTS.

关键词： encoder-decoder structure global-local transformer remote sensing (RS) semantic segmentation

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Cascaded transformer U-net for image restoration

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SIGNAL PROCESSING 2023年第1期206卷

作者： Yan, Longbin Zhao, Min Liu, Shumin Shi, Shuaikai Chen, Jie Northwestern Polytech Univ Shenzhen Res & Dev Inst Shenzhen Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Blueye Intelligence Zhenjiang Peoples R China

Image restoration is one of the most important computer vision tasks, aiming at recovering high-quality images from degraded or low-quality observations. The restoration methods based on convolutional neural networks (CNNs) have achieved attractive performance, however, as convolutions only intake local information, CNN-based methods have limitations in modeling objects in long ranges and extracting global information. In addition, existing one-stage methods damage the performance due to lacking diversified receptive fields. In this paper, we propose a multi-stage cascaded transformer architecture for image restoration. Firstly, the Swin transformer based encoder relying on self-attention is used to improve the modeling ability for long-range objects and outputs hierarchical multi-level semantic features. Then, a shape perceiving module is designed and embedded in the decoder to enhance the representation of irregular objects, Moreover, a multi-stage cascaded encoder-decoder architecture possessing diversified receptive fields is proposed to progressively obtain fine restoration results and thus boost the performance. We conduct extensive experiments, including image deraining, underwater image enhancement, near infrared image colorization and low-light image enhancement. The results show that our proposed method can achieve comparable or better performance than state-of-the-art methods while with less training and inference costs. (c) 2022 Published by Elsevier B.V.

关键词： Image deraining Underwater image enhancement Near infrared image colorization encoder-decoder structure Long -range dependence modeling

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Attention Aggregation encoder-decoder Network Framework for Stereo Matching

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IEEE SIGNAL PROCESSING LETTERS 2020年 27卷 760-764页

作者： Zhang, Yaru Li, Yaqian Kong, Yating Liu, Bin Yanshan Univ Sch Informat Sci & Engn Qinhuangdao 066004 Hebei Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China

In the stereo matching networks based on deep learning, current cost aggregation networks lack the means to aggregate cost volume to the utmost extent. Therefore, different from the standard encoder-decoder structures, we propose an attention aggregation encoder-decoder network framework for stereo matching that contains three modules. Specifically, we design a sub-branch and cross-stage aggregation encoding module, which aggregate context information of different sub-branches and cross-stages to achieve the mutual utilization of different deep cost volumes. Meanwhile, we introduce a three-dimensional attention recoding module to obtain the robust discriminative cost volume through recalibrating the high-level semantic information of the sub-branches. In addition, we construct a stepwise aggregation decoding module to decode the cost volume via the stepwise fusion upsampling strategy, which further enhances the learning ability of the network model. The experimental results on Scene Flow and KITTI benchmark datasets show that the proposed network framework is superior to other similar methods in aggregating information.

关键词： Three-dimensional displays Encoding Training Semantics Decoding Feature extraction Convolution Attention mechanism deep learning encoder-decoder structure stereo matching

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