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encoder-decoder network with Depthwise Atrous Spatial Pyramid Pooling for Automatic Brain Tumor Segmentation

NEURAL PROCESSING LETTERS 2023年第2期55卷 1697-1713页

作者： AboElenein, Nagwa M. Piao, Songhao Zhang, Zhehong Harbin Inst Technol Sch Comp Sci & Technol Harbin 150001 Peoples R China Menoufia Univ Fac Comp & Informatd Shibin Al Kawm 32511 Egypt

The accurate automated brain tumor segmentation is important for disease analysis and control and increases the likelihood of survival. However, it faces significant challenges due to the low contrast of the tissue boundary and the small size of the tumor. Convolutional Neural networks are a common automated image evaluation technique that has greatly improved current state-of-the-art precision in the task of segmenting brain tumors. This paper presents an advanced encoder-decoder algorithm with Depthwise Atrous Spatial Pyramid Pooling network (EDD-Net). Firstly, Dilated-ResNet block with Squeeze-and Excitation is introduced in the encoder and decoder module to derive image features adaptively and focuses on the relevant characteristics of the brain segmentation task with fewer parameters. Then, the Depthwise atrous Spatial Pyramid Pooling(DSPP) technique is used as the transition and output layers of the network, to achieve the multi-scale extraction of the feature image and preserves more spatial information. Furthermore, to speed up learning we propose a down-sampling module, while the up-sampling module can more efficiently aggregate low- and high-level feature information. The proposed method is evaluated on the Brats 19 dataset. Experiments demonstrate that the EDD-Net provides high accuracy and robustness in small tumor segmentation. On the online validation set, the suggested ensemble achieved dice scores of 0.813, 0.873, and 0.866 for tumor enhancement, whole tumor enhancement, and tumor core enhancement, respectively, performing favorably compared with existing state-of-the-art architectures.

关键词： encoder-decoder network Dilated residual network Squeeze-and-excitation Atrous spatial pyramid pooling Brain tumor segmentation

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encoder-decoder network with RMP for tongue segmentation

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MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING 2023年第5期61卷 1193-1207页

作者： Kusakunniran, Worapan Borwarnginn, Punyanuch Karnjanapreechakorn, Sarattha Thongkanchorn, Kittikhun Ritthipravat, Panrasee Tuakta, Pimchanok Benjapornlert, Paitoon Mahidol Univ Fac Informat & Commun Technol Nakhon Pathom Thailand Mahidol Univ Fac Engn Dept Biomed Engn Nakhon Pathom Thailand Mahidol Univ Fac Med Ramathibodi Hosp Dept Rehabil Med Bangkok Thailand

Tongue and its movements can be used for several medical-related tasks, such as identifying a disease and tracking a rehabilitation. To be able to focus on a tongue region, the tongue segmentation is needed to compute a region of interest for a further analysis. This paper proposes an encoder-decoder CNN-based architecture for segmenting a tongue in an image. The encoder module is mainly used for the tongue feature extraction, while the decoder module is used to reconstruct a segmented tongue from the extracted features based on training images. In addition, the residual multi-kernel pooling (RMP) is also applied into the proposed network to help in encoding multiple scales of the features. The proposed method is evaluated on two publicly available datasets under a scenario of front view and one tongue posture. It is then tested on a newly collected dataset of five tongue postures. The reported performances show that the proposed method outperforms existing methods in the literature. In addition, the re-training process could improve applying the trained model on unseen dataset, which would be a necessary step of applying the trained model on the real-world scenario.

关键词： Tongue segmentation encoder-decoder network RMP Separable convolution

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A Symmetry-Aware Pitch Feature encoder-decoder network for Condition Monitoring of Pitch Bearings in Wind Turbines

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IEEE SENSORS JOURNAL 2025年第8期25卷 14379-14392页

作者： Jiang, Guoqian Zhao, Kai Bai, Jiarong Yue, Jian Ding, Xuejuan Xie, Ping Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Peoples R China Beijing Goldwind Smart Energy Technol Co Ltd Beijing 100176 Peoples R China

Pitch bearing is one of the critical components of the electric pitch system in wind turbines (WTs), and its early and reliable fault warning is of great significance to ensure the operational reliability and safety of the entire system. In this article, we develop a condition monitoring system for pitch bearing with available supervisory control data acquisition (SCADA) data. Motivated by the three-blade symmetrical structure of WTs, we propose a new symmetry-aware pitch feature encoder-decoder network named PitchNet. A group feature encoding strategy is first designed to extract intragroup variable-wise feature information. Then, a feature attention mechanism is applied to discover important intergroup features by dynamically assigning different weights to learned pitch representations. Finally, a feature decoder is used to reconstruct the pitch input features and derive the reconstruction residuals for early fault detection. The proposed PitchNet is evaluated using real cases of pitch-bearing cracks. Results have demonstrated that the proposed PitchNet presents better early fault warning ability and lower false alarm rates (FARs), which can provide promising and reliable monitoring results for wind farm operators.

关键词： Feature extraction Sensors Motors Inverters Wind speed Reliability Mathematical models Wind turbines Condition monitoring Blades encoder-decoder network feature attention group feature learning supervisory control data acquisition (SCADA) data wind turbine (WT) pitch bearing

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Laser line scanner based real-time geometry monitoring using encoder-decoder network during Directed Energy deposition

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MEASUREMENT 2025年 239卷

作者： Yang, Liu Wang, Boyu Liu, Peipei Jeon, Ikgeun Chen, Zhenyi Li, Mingkai Xiong, Yilei Cheng, Jack C. P. Sohn, Hoon Korea Adv Inst Sci & Technol KAIST Dept Civil & Environm Engn Daejeon 34141 South Korea Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Kowloon Clear Water Bay Hong Kong Peoples R China Korea Adv Inst Sci & Technol KAIST Ctr 3D Printing Nondestruct Testing Daejeon 34141 South Korea Southeast Univ Sch Civil Engn Nanjing 210096 Peoples R China Korea Inst Machinery & Mat Dept Laser & Electron Beam Technol Daejeon 34103 South Korea

Directed Energy Deposition (DED) is a metal additive manufacturing technology that is gaining popularity for its ability to rapidly manufacture virtually any metal components no matter how complex the shapes and properties are. However, the current lack of real-time geometry monitoring and control is hindering the wider dissemination of DED in industries. This study developed and validated a geometry monitoring methodology which can achieve real-time inspection of the melt pool and newly solidified layer, and layer-wise inspection of the deposited layer during DED process. An encoder-decoder network was developed and applied to the profile images from the laser line scanner to obtain track profiles. A point cloud generation method was proposed to convert the obtained track profiles into 3D point cloud data using intrinsic/extrinsic calibration and printing position. Experiments have been successfully conducted to validate the proposed methodology by depositing multi-layer X-shape objects.

关键词： Additive manufacturing Directed energy deposition encoder-decoder network Real-time geometry monitoring Laser line scanning

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Composite encoder-decoder network for rapid bridge damage assessment using long-term monitoring acceleration data

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2024年第6期23卷 3387-3415页

作者： Yessoufou, Fadel Yang, Yibo Zhu, Jinsong Tianjin Univ State Key Lab Hydraul Engn Intelligent Construct & Tianjin Peoples R China CCCC First Highway Engn Grp Co Ltd Beijing Peoples R China Tianjin Univ Key Lab Coast Civil Struct Safety Minist Educ Tianjin Peoples R China Tianjin Univ State Key Lab Hydraul Engn Intelligent Construct & 135 Yaguan Rd Tianjin 300072 Peoples R China

In recent years, the deployment of structural health monitoring (SHM) systems has become paramount for safeguarding critical infrastructures. Notwithstanding, the development of an unsupervised deep learning framework capable of learning from long-term sensor data remains a critical challenge, particularly in accurately assessing the exact damage location. This study addresses this gap by proposing a novel approach for rapid bridge damage assessment. The proposed method employed a deep overcomplete encoder-decoder network (DOEDN) to reconstruct the acceleration data acquired from each sensor node on the bridge. The reconstruction losses generated by the DOEDN framework are then used as damage-sensitive features. Additionally, a damage indicator based on Gaussian processes is introduced to assess the damage location and evaluate its severity. The performance and sensitivity of the proposed DOEDN framework are evaluated through long-term monitoring acceleration data from a numerical highway bridge model and the well-known full-scale Z24 bridge. Furthermore, comparative assessments against the regular deep undercomplete encoder-decoder network are conducted using metrics including mean absolute error, coefficient of determination (R2), and mean intersection over union. The results show that the proposed DOEDN framework can reasonably assess the damage location and evaluate its severity across various structural scenarios in the bridge, even in the presence of temperature variations, thus providing a practical and effective solution for bridge health monitoring.

关键词： Structural health monitoring deep learning encoder-decoder network bridge damage assessment long-term monitoring data

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Adversarial Partial Discharge Signal Reconstruction and Denoising With an encoder-decoder network

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IEEE ACCESS 2024年 12卷 132271-132278页

作者： Firuzi, Keyvan Middle East Tech Univ METU Dept Elect & Elect Engn TR-06800 Ankara Turkiye

As the application of online partial discharge (PD) measurement increases the importance of denoising becomes increasingly obvious. Besides denoising a PD signal to detect and calculate discharge amplitude, pulse reconstructing is required to compute rise time, fall time and other features which play a crucial role in determining discharge severity and identifying defect types. In this paper, a deep learning method based on adversarial deep network by using encoder-decoder network as a generator is developed to perform a denoising and signal reconstruction task. The issue is to provide a database that contains both noisy and denoised data because every denoising method has its limitation, and it is not possible to train a network with real data and their perfect denoised pulses. Therefore, a synthetic database is developed, and signal deformation and noise added synthetically. The trained network's performance is evaluated under actual conditions in two distinct laboratories on various days, with differing noise levels and waveforms. The proposed method outperforms the wavelet method in denoising synthetic data and shows an improvement on real data, while successfully reconstructing the PD pulses. Enhancing the network's performance further, it underwent fine-tuning with actual noise, which led to a marked enhancement in its denoising ability and overall capabilities.

关键词： Partial discharges denoising signal reconstruction signal reconstruction wavelet denoising wavelet denoising encoder-decoder network encoder-decoder network adversarial network adversarial network encoder-decoder network adversarial network

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SAR marine oil spill detection based on an encoder-decoder network

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2024年第2期45卷 587-608页

作者： Soh, Kexin Zhao, Lingli Peng, Min Lu, Jianzhong Sun, Weidong Tongngern, Suchada Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan Peoples R China Wuhan Univ Sch Remote Sensing & Informat Engn Wuhan Peoples R China Shenyang Geotech Invest & Surveying Res Inst Co Lt Shenyang Peoples R China

Marine oil spills are currently among the most challenging problems in marine environments. Synthetic Aperture Radar (SAR), with all-day and all-weather observation, has demonstrated great potential in oil disaster monitoring. A simple encoder-decoder network model was proposed by using SAR images for effective oil spill detection. The proposed model incorporates an optimized U-Net architecture that reduces computational requirements while maintaining detection performance. This is achieved through shortened encoder and decoder stages, depthwise separable convolutions, group normalization, and bilinear interpolation-based upsampling. To improve the model's generalization, the auto-learning rate and focal loss function are also included. Two public SAR datasets acquired by different sensors have been used to illustrate the efficiency of the proposed model. In addition, the polarimetric information has also been assessed for the detection of oil spill monitoring. The results show that the model can achieve high efficiency at a small model size. The sub-dataset with polarimetric features also achieves a high F1-score of 91.65% and an IoU of 84.59%.

关键词： SAR oil spill detection encoder-decoder network U-Net model polarization

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Internal and external transmission encoder-decoder network for single-image deraining

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VISUAL COMPUTER 2024年第12期40卷 8653-8663页

作者： Xu, Yingcheng Han, Congwei Lv, Shuqi Wang, Ze Wang, Miao Shandong Univ Finance & Econ Sch Comp Sci & Technol Jinan 250014 Peoples R China

The visual quality of images captured in rainy weather is degraded by rain streaks, which may significantly reduce the accuracy of computer vision systems. In this paper, an internal and external transmission encoder-decoder network is proposed for eliminating rain streaks in a single image. Since rain streaks and background structures tend to be spatially long, we apply an internal connected aggregation unit to leverage global information and aggregate features at different scales to help remove rain streaks while restoring scene details. To fully exploit effective features of the encoder network and remove redundant information, we utilize an external connected enhancement unit to obtain effective feature maps for predicting clear outputs. By utilizing the efficient information inside and outside the unit, the proposed method can remove the rain streaks of different sizes and directions in the image and restore clear background structure details. Extensive experimental results on synthetic datasets and natural samples show that our method can achieve better rain removal performance than other advanced methods.

关键词： Internal and external transmission encoder-decoder network Image deraining

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Efficient-Unet: Intelligent identification of abrasive grain on the entire surface of monolayer brazing wheel based on encoder-decoder network

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2024年第12期131卷 6027-6037页

作者： Chen, Junying Wang, Boxuan Lin, Yiming Chen, Xiuyu Jiang, Qingshan Cui, Changcai Jimei Univ Coll Marine Equipment & Mech Engn Xiamen 361021 Peoples R China Huaqiao Univ Inst Mfg Engn Xiamen 361021 Peoples R China

Measuring and extracting abrasive grains on the entire surface of monolayer brazing grinding wheels to analyze the distribution of abrasive grains are of great significance to grinding research and grinding wheel manufacture. It is not easy to carry out the work with traditional methods. In this paper, a linear CCD is used to acquire the entire grinding wheel surface image, and an improved encoder-decoder network based on Efficientnet, ASPP, and Skip Connections is designed to promote the accuracy and speed of abrasive grains prediction. Based on dataset creation, transfer learning, and hyper-parameter testing, 91.21% abrasive grain semantic segmentation accuracy was finally obtained. If we focus on whether the abrasive grains are recognized without considering semantic errors, an accuracy rate of 99.6% is obtained. The method can provide basic data for grinding mechanism research and abrasive tool manufacturing.

关键词： Grinding wheel abrasive grain Intelligent identification encoder-decoder network ASPP

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IMG2HEIGHT: height estimation from single remote sensing image using a deep convolutional encoder-decoder network

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INTERNATIONAL JOURNAL OF REMOTE SENSING 2023年第18期44卷 5686-5712页

作者： Du, Shouhang Xing, Jianghe Du, Shihong Cui, Ximin Xiao, Xiongwu Li, Wei Wang, Shaoyu China Univ Min & Technol Beijing Coll Geosci & Surveying Engn Beijing Peoples R China Shanghai Surveying & Mapping Inst Key Lab Spatial Temporal Big Data Anal & Applicat Shanghai Peoples R China Peking Univ Inst Remote Sensing Beijing Peoples R China Peking Univ GIS Beijing Peoples R China Wuhan Univ State Key Lab Informat Engn Surveying Mapping & Re Wuhan Peoples R China

Height estimation from single remote sensing image is a challenging inherently ambiguous and technically ill-posed problem that we address in this study by resorting to deep learning approach. A spatial enhanced and multi-scale aggregated encoder-decoder network, SM-EDNet, is proposed, which takes a single image as input and produces an estimated height map as output. First, residual network (ResNet) is applied to extract low-level and deep features to cope with the heterogeneous characteristics of remote sensing scenes. Then, the multi-scale context information is aggregated through DenseASPP (Dense Atrous Spatial Pyramid Pooling) by extracting features from multiple dilated convolution layers. The skip connection is constructed by using the structure preserving model, DULR, to aggregate ResNet low-level features and multi-scale high-level features. The deformable convolution module is constructed to enhance the sensitivity to differences in geometric shapes of ground objects. For model training, three-layer deep supervision mechanism is designed to counteract the adverse effects of unstable gradients changes. Experimental results on three benchmark datasets, including ISPRS Vaihingen, ISPRS Potsdam, and DFC2018, show that the proposed method achieves the most outstanding performance compared with the state-of-the-art networks. The source codes are available at: https://***/xjh0929/2HEIGHT.

关键词： Single remote sensing image height estimation encoder-decoder network DenseASPP deep supervision mechanism ResNet

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