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SVInvNet: A Densely Connected encoder-decoder architecture for Seismic Velocity Inversion

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Khatounabad, Mojtaba Najafi Yalim Keles, Hacer Kadioglu, Selma Ankara Univ Inst Sci & Technol Dept Geophys Engn TR-06830 Ankara Turkiye Hacettepe Univ Dept Comp Engn TR-06800 Ankara Turkiye Ankara Univ Dept Geophys Engn TR-06830 Ankara Turkiye

This study presents a deep learning (DL)-based approach to the seismic velocity inversion problem, focusing on both noisy and noiseless training datasets of varying sizes. Our seismic velocity inversion network (SVInvNet) introduces a novel architecture that contains a multiconnection encoder-decoder structure enhanced with dense blocks. This design is tuned to effectively process time series data, which is essential for addressing the challenges of nonlinear seismic velocity inversion. For training and testing, we created diverse seismic velocity models, including multilayered, faulty, and salt dome categories. We also investigated how different kinds of ambient noise, both coherent and stochastic, and the size of the training dataset affect learning outcomes. SVInvNet is trained on datasets ranging from 750 to 6000 samples and is tested using a large benchmark dataset of 12 000 samples. Despite its fewer parameters compared to the baseline model, SVInvNet achieves superior performance with this dataset. The performance of SVInvNet was further evaluated using the OpenFWI dataset and Marmousi-derived velocity models. The comparative analysis clearly reveals the effectiveness of the proposed architecture.

关键词： Data models Training Mathematical models Predictive models Salt Surface waves Decoding Computer architecture Benchmark testing Receivers Convolutional neural network (CNN) deep learning (DL) densenet encoder-decoder architecture seismic velocity inversion

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TSPCS-net: Two-stage pavement crack segmentation network based on encoder-decoder architecture

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 141卷

作者： Yue, Biao Dang, Jianwu Sun, Qi Wang, Yangping Min, Yongzhi Wang, Feng Lanzhou Jiaotong Univ Sch Automat & Elect Engn Lanzhou 730070 Gansu Peoples R China Lanzhou Jiaotong Univ Natl Virtual Simulat Expt Teaching Ctr Rail Trans Lanzhou 730070 Gansu Peoples R China Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Gansu Peoples R China Lanzhou Jiaotong Univ Key Lab Railway Ind BIM Engn & Intelligent Applic Lanzhou 730070 Gansu Peoples R China Gansu Rd & Bridge Feiyu Traff Facil Co Ltd Lanzhou 730050 Gansu Peoples R China

Crack segmentation is of great significance in automatic pavement crack detection based on image recognition. Although recent convolutional neural network (CNN)-based segmentation methods have shown promising performance, accurate pavement crack segmentation still faces some challenges, such as various crack sizes, class imbalance issues, and background interference. To overcome these challenges, a compact two-stage pavement crack segmentation network based on encoder-decoder architecture (TSPCS-Net) is proposed, which includes a classification network and a segmentation network. The classification network, consisting of a feature extraction module transferred from the segmentation network and a lightweight feature fusion module, is used to quickly classify and eliminate crack-free images that existed in large numbers in actual pavement image datasets. The segmentation network is constructed based on an encoder-decoder architecture for precise pixel-level segmentation of the samples determined as crack images. Specifically, to extract multi-scale crack features, a novel multi-scale encoder module is designed by combining dilated convolution and residual structure. Then, a left-side path (LSP) is designed to alleviate the influence of class imbalance on feature extraction. Finally, an attention module with high-dimensional features guiding low-dimensional features (AM-HGL) is proposed to focus on crack-relevant features and suppress interference information. The effectiveness of the proposed TSPCS-Net is validated on a self-made unmanned aerial vehicles pavement crack (UAVPC) dataset and two public pavement distress datasets, and extensive experiments show that the proposed method outperforms current state-of-the-art methods in terms of segmentation performance and efficiency, which can meet the needs of pavement crack segmentation in practical application scenarios.

关键词： Pavement crack segmentation encoder-decoder architecture Transfer learning Multi-scale feature extraction Attention mechanism

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A deep convolutional encoder-decoder architecture for autonomous fault detection of PV plants using multi-copters

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SOLAR ENERGY 2021年 223卷 217-228页

作者： Sizkouhi, Amirmohammad Moradi Aghaei, Mohammadreza Esmailifar, Sayyed Majid Amirkabir Univ Technol Dept Aerosp Engn Tehran 158754413 Iran Eindhoven Univ Technol Energy Technol Grp Dept Mech Engn NL-5612 AE Eindhoven Netherlands Albert Ludwigs Univ Freiburg Dept Sustainable Syst Engn INATECH Solar Energy Engn Fac Engn D-79110 Freiburg Germany

This study presents an autonomous fault detection method for a wide range of common failures and defects which are visually visible on PV modules. In this paper, we focus especially on detection of bird's drops as a very typical defect on the PV modules. As a crucial prerequisite, a data-set of aerial imageries of the PV strings affected by bird's drops were collected through several experimental flight by multi-copters in order to train an accurate fully convolutional deep network. These images are divided into three groups, namely, training, testing, and validation parts. For the purpose of bird's drops segmentation, an improved encoder-decoder architecture is employed. In this regard, a modified VGG16 model is used as a backbone for the encoder part. The encoder of the network has a very flexible architecture that can be modified and trained for any other visual failure detection. Later on, extracted feature maps of images are imported into a decoder network to map the low resolution features to full resolution ones for pixel-wise segmentation. In addition, an image object positioning algorithm is presented to find the exact position of detected failures in local coordinate system. In a post-processing step, the detected damages are prioritized based on various parameters such as severity of shading and extent of impact on the PV module's output current. For further validation, different affected PV modules were characterized according to the output patterns of the classification step in order to accurately evaluate the effect of birds' drops and consequent shading on the parameters of PV modules based on their severity and location. Finally, the training and testing results demonstrate that the proposed FCN network is able to predict precisely covered pixels by bird's drops on PV modules at pixel level with average accuracies of 98% and 93% for training and testing, respectively.

关键词： Photovoltaic (PV) plants Autonomous monitoring Fault detection Fully Convolutional Network (FCN) Multi copter Aerial imagery encoder-decoder architecture

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U-Net: A valuable encoder-decoder architecture for liver tumors segmentation in CT images

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JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2022年第1期30卷 45-56页

作者： Sahli, Hanene Ben Slama, Amine Labidi, Salam Univ Tunis Lab Signal Image & Energy Mastery SIME ENSIT LR13ES03 Tunis 1008 Tunisia Univ Tunis EL Manar Lab Biophys & Med Technol ISTMT LR13ES07 Tunis 1006 Tunisia

This study proposes a new predictive segmentation method for liver tumors detection using computed tomography (CT) liver images. In the medical imaging field, the exact localization of metastasis lesions after acquisition faces persistent problems both for diagnostic aid and treatment effectiveness. Therefore, the improvement in the diagnostic process is substantially crucial in order to increase the success chance of the management and the therapeutic follow-up. The proposed procedure highlights a computerized approach based on an encoder-decoder structure in order to provide volumetric analysis of pathologic tumors. Specifically, we developed an automatic algorithm for the liver tumors defect segmentation through the Seg-Net and U-Net architectures from metastasis CT images. In this study, we collected a dataset of 200 pathologically confirmed metastasis cancer cases. A total of 8,297 CT image slices of these cases were used developing and optimizing the proposed segmentation architecture. The model was trained and validated using 170 and 30 cases or 85% and 15% of the CT image data, respectively. Study results demonstrate the strength of the proposed approach that reveals the superlative segmentation performance as evaluated using following indices including F1-score = 0.9573, Recall = 0.9520, IOU = 0.9654, Binary cross entropy = 0.0032 and p-value <0.05, respectively. In comparison to state-of-the-art techniques, the proposed method yields a higher precision rate by specifying metastasis tumor position.

关键词： Liver tumors CT images segmentation deep transfer learning encoder-decoder architecture

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Arabic Optical Character Recognition Using Attention Based encoder-decoder architecture 2

Arabic Optical Character Recognition Using Attention Based E...

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3rd International Conference on Artificial Intelligence, Robotics and Control (AIRC)

作者： Sobhi, Mohamed Hifny, Yasser Elkaffas, Saleh Mesbah Arab Acad Sci Technol & Maritime Transport Coll Comp & IT Kerdasa Egypt Univ Helwan Cairo Egypt

ISBN: (纸本)9781450389266

Optical character recognition (OCR) systems are used to convert scanned documents into text. Arabic OCR is an active area of research where high accuracy is demanding. This paper focuses on building a model for converting images that contain Arabic text into their corresponding text using a deep learning approach. This model does not require any knowledge of the underlying language and it is simply trained end-to-end on the KAFD dataset. It combines several standard neural components from vision and natural language processing. Features are extracted from images using Convolutional Neural Networks (CNNs) where the features are arranged in a grid. Each row is then encoded using a Recurrent Neural Networks (RNNs). An RNN decoder with a visual attention mechanism is used to generate the output text. Our preliminary experiments show that the presented approach is effective. The overall obtained accuracy is 89.82%. However, the individual results for some fonts are higher than this score.

关键词： Sequence to Sequence Model encoder-decoder architecture Arabic OCR Convolutional Neural Networks (CNNs) Recurrent Neural Network (RNN) Attention Mechanism Long Short-Term Memory (LSTM)

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Delineation of ECG waveform components using encoder-decoder architecture with Postprocess algorithm

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International Journal of Information Technology (Singapore) 2024年第6期16卷 3425-3435页

作者： Sharma, Deepti Kohli, Narendra Department of Computer Science and Engineering Harcourt Butler Technical University HBTU East Campus Nawabganj Uttar Pradesh Kanpur 208002 India

With the exponential increase in heart disease cases, it is essential to construct models (algorithms) that can be used to delineate Electrocardiogram (ECG/EKG) wave components. ECG delineation is the process of attaining structural and biological information of every wave component in a signal in terms of finding out the endpoints. This work intends to develop a deep learning model to delineate the P, QRS, T, and U segments and a post-processing algorithm to remove redundant peaks. Thus, the model will help cardiologists achieve better prediction of diseases efficiently. The proposed model is based on encoder-decoder based deep learning (DL) architecture developed to find the onsets and offsets of the various waves occurring in a single heartbeat. Here, a publicly available QT dataset (QTDB) is used as this is the only dataset with U wave annotation. The post-processing method uses the ECG signal's morphological information to remove the effect of incorrectly categorized focal points of various wave components. It identifies the prominent peak by eliminating redundant peaks. The results of the delineation performance are satisfying, with an average sensitivity of 98.45%, and a precision of 91.40% respectively. These findings point to potential uses for wireless and wearable health monitoring technology. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： 1D U-Net Deep learning ECG delineation encoder-decoder architecture U wave delineation

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Lightweight encoder-decoder architecture for Foot Ulcer Segmentation 28th

Lightweight Encoder-Decoder Architecture for Foot Ulcer Segm...

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28th International Workshop on Frontiers of Computer Vision (IW-FCV)

作者： Ali, Shahzad Mahmood, Arif Jung, Soon Ki Kyungpook Natl Univ KNU Sch Comp Sci & Engn Daegu South Korea Informat Technol Univ ITU Dept Comp Sci Lahore Pakistan

ISBN: (纸本)9783031063817;9783031063800

Continuous monitoring of foot ulcer healing is needed to ensure the efficacy of a given treatment and to avoid any possibility of deterioration. Foot ulcer segmentation is an essential step in wound diagnosis. We developed a model that is similar in spirit to the well-established encoder-decoder and residual convolution neural networks. Our model includes a residual connection along with a channel and spatial attention integrated within each convolution block. A simple patch-based approach for model training, test time augmentations, and majority voting on the obtained predictions resulted in superior performance. Our model did not leverage any readily available backbone architecture, pre-training on a similar external dataset, or any of the transfer learning techniques. The total number of network parameters being around 5 million made it a significantly lightweight model as compared with the available state-of-the-art models used for the foot ulcer segmentation task. Our experiments presented results at the patch-level and image-level. Applied on publicly available Foot Ulcer Segmentation (FUSeg) Challenge dataset from MICCAI 2021, our model achieved state-of-theart image-level performance of 88.22% in terms of Dice similarity score and ranked second in the official challenge leader-board. We also showed an extremely simple solution that could be compared against the more advanced architectures.

关键词： Medical image segmentation Foot ulcer segmentation Attention mechanism encoder-decoder architecture

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Investigation and Improvement of VGG based encoder-decoder architecture for Background Subtraction

Investigation and Improvement of VGG based Encoder-Decoder A...

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Advanced Communication Technologies and Signal Processing (IEEE ACTS)

作者： Rabidas, Rinku Ravi, Dheeraj Kr Pradhan, Shashikant Moudgollya, Rhittwikraj Ganguly, Amrita Assam Univ Dept ECE Silchar India Assam Engn Coll Dept Instrumentat Engn Gauhati India Assam Engn Coll Dept Elect Engn Gauhati India

ISBN: (纸本)9781728170978

Object detection in motion pictures is always a challenging task due to the presence of dynamic background. Deep learning architectures especially encoder-decoder type has shown promising performance in segmenting foreground objects against the background in video sequences. Thus, in this work, a VGG-16 based encoder-decoder architecture is investigated and several modifications are proposed to improve the efficiency the model. The modified models are evaluated on two different standard databases- CDNet 2014 and SBI2015 with various scenes and achieved the highest precision of 0.99 which is competitive in nature with the current schemes in the state-of-the-art.

关键词： Background subtraction Deep learning encoder-decoder architecture Feature pooling

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A novel approach to ultra-short-term multi-step wind power predictions based on encoder-decoder architecture in natural language processing

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JOURNAL OF CLEANER PRODUCTION 2022年 354卷

作者： Wang, Lei He, Yigang Li, Lie Liu, Xiaoyan Zhao, Yingying Wuhan Univ Sch Elect Engn & Automat Wuhan 430072 Peoples R China

Accurate wind power predictions (WPPs) are highly significant to the safety, stability, and economic operation of power systems. The reported encoder--decoder architectures have demonstrated clear advantages over traditional methods in multi-step WPP tasks. However, the reported frameworks still have defects involving insufficient information mining abilities and low computing efficiencies. To address these shortcomings, this study proposed three improved encoder-decoder architectures, sequence-to-sequence bidirectional gated recurrent unit (SBIGRU), attention-based sequence-to-sequence Bi-GRU (ASBIGRU) and Transformer, in natural language processing for multi-step WPP. Data, including numerical weather predictions and wind powers, from 12 wind farms located in 12 different regions of China were used to validate our proposed models. The correlations between the datasets from multiple wind farms were analyzed using Pearson's correlation coefficient method to demonstrate the feasibility of our proposed models even without considering the spatial correlations. We adopted an effective strategy combining manual experience and machine grid searches to define the hyper-parameters needed to optimize the performance of our proposed models. The prediction accuracies and computational efficiencies of the reported and proposed models were compared experimentally. For prediction accuracy, the experimental results showed that, compared with existing models, Transformer, ASBIGRU and SBIGRU reduced the root mean square error by 3.21%, 1.06% and 0.88% in 16-step-ahead predictions, respectively. Furthermore, for computational efficiency, the training time of the existing model at a wind farm is 3.57 times that of Transformer. This confirmed that the Transformer model performs better in terms of prediction accuracy and computational efficiency. Our work illustrates the potential of Transformer for large-scale wind farm applications.

关键词： Wind power prediction Hyper-parameter setting encoder-decoder architecture NWP Transformer

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Improving the Efficiency of encoder-decoder architecture for Pixel-Level Crack Detection

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IEEE ACCESS 2019年 7卷 186657-186670页

作者： Chen, Hanshen Lin, Huiping Yao, Minghai Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Zhejiang Inst Commun Hangzhou 311112 Peoples R China Zhejiang Univ Affiliated Hosp 1 Coll Med Dept Stomatol Hangzhou 310003 Peoples R China

Cracks are one of the most common categories of pavement distress that may potentially threaten road and highway safety. Thus, a reliable and efficient pixel-level method of crack detection is necessary for real-time measurement of the crack. However, many existing encoder-decoder architectures for crack detection are time-consuming because the part of decoder module always has lots of convolutional layers and feature channels that lead to performance that highly relies on computing resources, which is a handicap in scenarios with limited resources. In this study, we propose a simple and effective method to boost the algorithmic efficiency based on encoder-decoder architecture for crack detection. We develop a switch module, called SWM, to predict whether the image is positive or negative and then skip the decoder module to save computation time when it is negative. This method uses the encoder module as the fixed feature extractor and only needs to place a light-weight classifier head on the end of the encoder module to output the final class probability. We choose the classical UNet and DeepCrack as examples of the encoder-decoder architectures to show how SWM is integrated into the architectures to reduce computation complexity. Evaluations on the public CrackTree206 and AIMCrack datasets demonstrate that our method can significantly boost the efficiency of the encoder-decoder architectures in all tasks, while without affecting the performance. The SWM can also be easily embedded into other encoder-decoder architectures for further improvement. The source code is available at https://***/hanshenchen/crack-detection.

关键词： Computer vision deep learning encoder-decoder architecture crack detection

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