Convolution Technique Research Articles

Automated image acquisition and analysis of graphene and hexagonal boron nitride from pristine to highly defective and amorphous structures.

Defect-engineered and even amorphous two-dimensional (2D) materials have recently gained interest due to properties that differ from their pristine counterparts. Since these properties are highly sensitive to the exact atomic structure, it is crucial to be able to characterize them at atomic resolution over large areas. This is only possible when the imaging process is automated to reduce the time spent on manual imaging, which at the same time reduces the observer bias in selecting the imaged areas. Since the necessary datasets include at least hundreds if not thousands of images, the analysis process similarly needs to be automated. Here, we introduce disorder into graphene and monolayer hexagonal boron nitride (hBN) using low-energy argon ion irradiation, and characterize the resulting disordered structures using automated scanning transmission electron microscopy annular dark field imaging combined with convolutional neural network-based analysis techniques. We show that disorder manifests in these materials in a markedly different way, where graphene accommodates vacancy-type defects by transforming hexagonal carbon rings into other polygonal shapes, whereas in hBN the disorder is observed simply as vacant lattice sites with very little rearrangement of the remaining atoms. Correspondingly, in the case of graphene, the highest introduced disorder leads to an amorphous membrane, whereas in hBN, the highly defective lattice contains a large number of vacancies and small pores with no indication of amorphisation. Overall, our study demonstrates that combining automated imaging and image analysis is a powerful way to characterize the structure of disordered and amorphous 2D materials, while also illustrating some of the remaining shortcomings with this methodology.

ECG signal reconstruction from PPG using a hybrid attention-based deep learning network

Electrocardiography (ECG) and photoplethysmography (PPG) are non-invasive methods used to quantify signals originating from the cardiovascular system. Although there is a strong correlation between the cycles of the two measures, the correlation between the waveforms has received little attention in research. Measuring the PPG is significantly simpler and more convenient compared to the ECG measure. Recent research has demonstrated that PPG signals can be utilized to rebuild the ECG signals, suggesting that healthcare professionals might acquire a comprehensive comprehension of patients' cardiovascular well-being by only measuring PPG. With the advancement of artificial intelligence, the deep learning model has made significant progress in reconstructing ECG signals from PPG signals. However, the quality of the reconstructed ECG signal is still in need of more enhancement. In order to enhance the quality of the reconstructed ECG signal, we propose an innovative hybrid attention-based bidirectional recurrent neural network (Bi-RNN) that incorporates a dilated convolutional neural network technique for the purpose of reconstructing ECG signals from PPG signals. This addresses issues that occur when standard dilated CNN (DCNN) models neglect the link between circumstances and dispersion of gradients. The suggested approach maximizes the utilization of the DCNN and Bi-RNN unit design to provide fusion features. In order to ensure the model's resilience to deformation, it is imperative to initially extract spatial properties utilizing convolutional neural networks (CNNs). Once we have obtained geographic data, we employ BiLSTM to extract temporal features. The proposed model is so-called hybrid attention-based CNN and BiLSTM (HA-CNN-BiLSTM). BiLSTM mitigates the issues of gradient vanishing and exploding while maintaining accuracy. To showcase the advantages of the suggested model, we conduct a comparison between HA-CNN-BiLSTM and each separate state-of-the-art method. The simulation results indicated that the proposed method yielded superior root mean square error (RMSE) when reconstructing the ECG signal across different optimizers. The results also demonstrated that the proposed method results in the lowest RMSE with SGDM optimizer which is 0.031.

Pressure and Temperature Prediction of Oil Pipeline Networks Based on a Mechanism-Data Hybrid Driven Method

To ensure the operational safety of oil transportation stations, it is crucial to predict the impact of pressure and temperature before crude oil enters the pipeline network. Accurate predictions enable the assessment of the pipeline’s load-bearing capacity and the prevention of potential safety incidents. Most existing studies primarily focus on describing and modeling the mechanisms of the oil flow process. However, monitoring data can be skewed by factors such as instrument aging and pipeline friction, leading to inaccurate predictions when relying solely on mechanistic or data-driven approaches. To address these limitations, this paper proposes a Temporal-Spatial Three-stream Temporal Convolutional Network (TS-TTCN) model that integrates mechanistic knowledge with data-driven methods. Building upon Temporal Convolutional Networks (TCN), the TS-TTCN model synthesizes mechanistic insights into the oil transport process to establish a hybrid driving mechanism. In the temporal dimension, it incorporates real-time operating parameters and applies temporal convolution techniques to capture the time-series characteristics of the oil transportation pipeline network. In the spatial dimension, it constructs a directed topological map based on the pipeline network’s node structure to characterize spatial features. Data analysis and experimental results show that the Three-stream Temporal Convolutional Network (TTCN) model, which uses a Tanh activation function, achieves an error rate below 5%. By analyzing and validating real-time data from the Dongying oil transportation station, the proposed hybrid model proves to be more stable, reliable, and accurate under varying operating conditions.

Fast‐MedNeXt: Accelerating the MedNeXt Architecture to Improve Brain Tumour Segmentation Efficiency

ABSTRACTWith the rapid development of medical imaging technology, 3D image segmentation technology has gradually become a mainstream method, especially in brain tumour detection and diagnosis showing its unique advantages. The technique makes full use of 3D spatial information to locate and analyze tumours more accurately, thus playing an important role in improving diagnostic accuracy, optimising treatment planning and promoting research. However, it also suffers from significant computational expenditure and delayed processing pace. In this paper, we propose an innovative optimisation scheme to address this problem. We thoroughly investigate the MedNeXt network and propose Fast‐MedNeXt, which aims to increase the processing speed while maintaining accuracy. First, we introduce the partial convolution (PConv) technique, which replaces the deep convolutional layers in the network. This improvement effectively reduces computation and memory requirements while maintaining efficient feature extraction. Second, based on PConv, we propose PConv‐Down and PConv‐Up modules, which are applied to the up‐sampling and down‐sampling modules to further optimise the network structure and improve efficiency. To confirm the efficacy of the approach, we carried out a sequence of tests in the multimodal brain tumour segmentation challenge 2021 (BraTS2021). By comparing with the MedNeXt series network, the Fast‐MedNeXt reduced the latency by 22.1%, 20.5%, 15.8%, and 11.4% respectively, while the average accuracy also increased by 0.475% and 0.2% respectively. These significant performance improvements demonstrate the effectiveness of Fast‐MedNeXt in 3D medical image segmentation tasks and provide a new and more efficient solution for the field.

SODD-YOLOv8: an insulator defect detection algorithm based on feature enhancement and variable row convolution

Abstract To address the detection challenges in defective images of insulators in transmission lines, including tiny object size, significant scale variations, a wide variety of defects, and complex background interference. In this study, an improved insulator defect detection algorithm is proposed, based on the YOLOv8s framework and combining feature enhancement and deformable convolution techniques. Firstly, to address the image feature distortion problem caused by aerial photography, a deformable convolutional feature extraction module (DCFEM) is introduced, which is designed to enhance the model’s ability to adapt to the local geometric deformation, so as to effectively recover the distorted feature information in the image. Moreover, to enhance the detection ability of the model for small objects, a small object feature enhancement module is designed, which adopts an efficient multi-scale attention mechanism, and aims to enhance the feature extraction ability of small objects, improve the sensitivity to small-size defects, and improve the detection accuracy. Eventually, to optimize the computational efficiency of the model, the average pooling-sparse convolution-batch normalization (BN) module is proposed. This module combines average pooling, sparse convolution and BN techniques to achieve a lightweight model while maintaining a high level of feature extraction capability. Experimental results on the China power line insulator dataset show that the improved model achieves a 4.3 percentage point improvement in the mAP metric compared to YOLOv8s, and the number of parameters in the model is reduced by 10%. The proposed scheme not only improves the accuracy and efficiency of defect detection, but also reduces the demand for computational resources, thus providing a more reliable and efficient solution for insulator defect detection in practical applications.

DBAII-Net with multiscale feature aggregation and cross-modal attention for enhancing infant brain injury classification in MRI.

MRI is pivotal in diagnosing brain injuries in infants. However, the dynamic development of the brain introduces variability in infant MRI characteristics, posing challenges for MRI-based classification in this population. Furthermore, manual data selection in large-scale studies is labor-intensive, and existing algorithms often underperform with thick-slice MRI data. To enhance research efficiency and classification accuracy in large datasets, we propose an advanced classification model. We introduce the Dual-Branch Attention Information Interactive Neural Network (DBAII-Net), a cutting-edge model inspired by radiologists' use of multiple MRI sequences. DBAII-Net features two innovative modules: (1) the Convolutional Enhancement Module (CEM), which leverages advanced convolutional techniques to aggregate multi-scale features, significantly enhancing information representation; and (2) the Cross-Modal Attention Module (CMAM), which employs state-of-the-art attention mechanisms to fuse data across branches, dramatically improving positional and channel feature extraction. Performances (accuracy, sensitivity, specificity, area under the curve, etc.) of DBAII-Net were compared with eight benchmark models for brain MRI classification in infants aged 6 months to 2 years. Utilizing a self-constructed dataset of 240 thick-slice brain MRI scans (122 with brain injuries, 118 without), DBAII-Net demonstrated superior performance. On a test set of approximately 50 cases, DBAII-Net achieved average performance metrics of 92.53% accuracy, 90.20% sensitivity, 94.93% specificity, and an area under the curve (AUC) of 0.9603. Ablation studies confirmed the effectiveness of CEM and CMAM, with CMAM significantly boosting classification metrics. DBAII-Net with CEM and CMAM outperforms existing benchmarks in enhancing the precision of brain MRI classification in infants, significantly reducing manual effort in infant brain research. Our code is available at https://github.com/jiazhen4585/DBAII-Net.

Improved YOLOv8n for Lightweight Ship Detection

Automatic ship detection is a crucial task within the domain of maritime transportation management. With the progressive success of convolutional neural networks (CNNs), a number of advanced CNN models have been presented in order to detect ships. Although these detection models have achieved marked performance, several undesired results may occur under complex maritime conditions, such as missed detections, false positives, and low detection accuracy. Moreover, the existing detection models endure large number of parameters and heavy computation cost. To deal with these problems, we suggest a lightweight ship model of detection called DSSM–LightNet based upon the improved YOLOv8n. First, we introduce a lightweight Dual Convolutional (DualConv) into the model to lower both the number of parameters and the computational complexity. The principle is that DualConv combines two types of convolution kernels, 3x3 and 1x1, and utilizes group convolution techniques to effectively reduce computational costs while processing the same input feature map channels. Second, we propose a Slim-neck structure in the neck network, which introduces GSConv and VoVGSCSP modules to construct an efficient feature-fusion layer. This fusion strategy helps the model better capture the features of targets of different sizes. Meanwhile, a spatially enhanced attention module (SEAM) is leveraged to integrate with a Feature Pyramid Network (FPN) and the Slim-neck to achieve simple yet effective feature extraction, minimizing information loss during feature fusion. CIoU may not accurately reflect the relative positional relationship between bounding boxes in some complex scenarios. In contrast, MPDIoU can provide more accurate positional information in bounding-box regression by directly minimizing point distance and considering comprehensive loss. Therefore, we utilize the minimum point distance IoU (MPDIoU) rather than the Complete Intersection over Union (CIoU) Loss to further enhance the detection precision of the suggested model. Comprehensive tests carried out on the publicly accessible SeaShips dataset have demonstrated that our model greatly exceeds other algorithms in relation to their detection accuracy and efficiency, while reserving its lightweight nature.

A Lightweight Model Enhancing Facial Expression Recognition with Spatial Bias and Cosine-Harmony Loss

This paper proposes a facial expression recognition network called the Lightweight Facial Network with Spatial Bias (LFNSB). The LFNSB model effectively balances model complexity and recognition accuracy. It has two key components: a lightweight feature extraction network (LFN) and a Spatial Bias (SB) module for aggregating global information. The LFN introduces combined channel operations and depth-wise convolution techniques, effectively reducing the number of parameters while enhancing feature representation capability. The Spatial Bias module enables the model to focus on local facial features while capturing the dependencies between different facial regions. Additionally, a new loss function called Cosine-Harmony Loss is designed. This function optimizes the relative positions of feature vectors in high-dimensional space, resulting in better feature separation and clustering. Experimental results on the AffectNet and RAF-DB datasets demonstrate that the LFNSB model achieves competitive recognition accuracy, with 63.12% accuracy on AffectNet-8, 66.57% accuracy on AffectNet-7, and 91.07% accuracy on RAF-DB, while significantly reducing the model complexity.

Real-time field disease identification based on a lightweight model

In the field of agricultural technology, rapid and accurate diagnosis of crop diseases is crucial for ensuring crop health and yield. However, most methods for automatic diagnosis of plant diseases are primarily based on high-computational-performance server platforms, which limits their application in real-world environments. This paper establishes a crop disease dataset collected from real field scenarios, which is utilized for training, validating the proposed models, and enhancing model generalization in existing research. It provides an accurate and practical database for future research endeavors. The key innovation of this study lies in the development of a lightweight convolutional neural network architecture with high real-time capabilities, characterized by its high precision and efficiency. The network abandons traditional deep convolution, adopting partial convolution and point-wise convolution techniques instead. This approach significantly reduces computational complexity while maintaining high accuracy, making the model highly suitable for application in resource-constrained real-time environments. The proposed method achieved an accuracy of 99.04 % on the open-source PlantVillage dataset and 92.82 % accuracy on a dataset with complex backgrounds constructed. Additionally, this paper also investigates the deployment of the proposed method and other mature technologies on mobile platforms. The focus of this evaluation is to compare the real-time performance on devices with limited computational resources, thereby verifying the model’s applicability in the real world.

Customizable 6 degrees of freedom grasping dataset and an interactive training method for graph convolutional network

The field of robotic grasping has seen significant progress with the development of deep learning and the creation of large-scale datasets like the Cornell Grasping Dataset (Jiang et al., 2011) and DexNet (Mahler et al., 2016). However, challenges persist due to the reliance on manually annotated datasets, limited by data scarcity, high costs, biases, and a lack of diversity in gripper types and three-dimensional information, hampering their effectiveness in real-world applications. To confront these issues, an innovative method was introduced for generating robotic grasping datasets in a simulated environment, eliminating the need for manual annotations. The method utilizes a highly realistic movement of the gripper, offering extensive customization options for a variety of gripper types. It also introduces detailed evaluation metrics specifically designed to assess different gripper designs, ensuring accurate and meaningful analysis of grasping efficacy. Further, it excels in simulating a wide range of industrial scenarios, significantly enhancing the dataset's diversity and applicability in real-world applications. In addition, an end-to-end grasping prediction network is introduced, which leverages advanced graph convolution techniques to predict optimal grasping points and orientations from point cloud. It also serves as an effective baseline for the proposed grasping dataset. Lastly, the authors propose a novel interactive training method for deep learning models driven by data generation, featuring real-time interaction between the model and the data generator with a rule-based strategy that optimizes the training workflow based on feedback. Experimental results demonstrate that the interactive training method enables models to achieve superior outcomes in a shorter timeframe compared to those trained using traditional methods.

Enhancing pixel-level crack segmentation with visual mamba and convolutional networks

Computer vision-based semantic segmentation methods are currently the most widely used for automated detection of structural cracks in buildings and pavements. However, these methods face persistent challenges in detecting fine cracks with small widths and in distinguishing cracks from background stains. This paper addresses these issues by introducing MambaCrackNet, a new network architecture for pixel-level crack segmentation. MambaCrackNet incorporates residual visual Mamba blocks and integrates visual Mamba and convolutional neural network-based segmentation techniques. This approach effectively enhances the detection of fine cracks, reduces misdetections of background stains, and remains robust to variations in patch size and training sample sizes, making it highly practical for engineering applications. On two open access crack datasets, MambaCrackNet outperformed mainstream crack segmentation models, achieving MIoU scores of 0.8939 and 0.8560 and F1-scores of 0.8817 and 0.8412.

Apple fruits categorizing based on deep convolutional neural network techniques

<p>For a variety of reasons, including the high degree of similarity between varieties of the same type of fruit, the requirement to train the technique on a large amount of data, and the type and number of features suitable for application, the use of computer vision techniques in the classification of fruits still faces many challenges. Additionally, the technique's effectiveness and speed both need to be improved. Deep conventional neural network (DCNN) approaches were required for all of these reasons. A proposed CNN model is described in this work. The suggested methodology is intended to quickly and accurately categorize thirteen groups of apple fruits. The proposed technique was based on training and testing the model on a maximum number of images of apple fruits, by increasing the number of database images tenfold, after augmentation was performed on the images. The technology also relied on good tuning of the hyperparameters. To further ensure the efficiency of training, validation was performed on 20% of the database. All results that demonstrate the high efficiency of the proposed model were reviewed. The results of the proposal were compared with the results of four related techniques. The results showed the great advantage of the proposed technology at all levels.</p>

Bridging spatio-temporal discontinuities in global soil moisture mapping by coupling physics in deep learning

The launch of Soil Moisture Active Passive (SMAP) satellite in 2015 has resulted in significant achievements in global soil moisture mapping. Nonetheless, spatiotemporal discontinuities in the soil moisture products have arisen due to the limitations of its orbit scanning gap and retrieval algorithms. To address these issues, this paper presents a physics-constrained gap-filling method, PhyFill for short. The PhyFill method employs a partial convolutional neural network technique to explore spatial domain features of the original SMAP soil moisture data. Then, it incorporates variations in soil moisture induced by precipitation events and dry-down events as penalty terms in the loss function, thereby accounting for monotonicity and boundary constraints in the physical processes governing the dynamic fluctuations of soil moisture. The PhyFill model was applied to SMAP soil moisture data, resulting in continuous spatially daily soil moisture data on a global scale. Three validation strategies are employed: visual inspection through global pattern, simulated missing-region validation, and soil moisture validation with in situ measurements. The results indicated that the reconstructed soil moisture achieved a higher spatial coverage with satisfactory spatial continuity with neighbouring pixels. The simulated validation of the missing regions revealed that the averaged unbiased root mean square difference (ubRMSD) and correlation coefficient (R) were 0.01 m3/m3 and 0.99, respectively versus the gap filled SMAP product. The core validation sites demonstrated that the reconstructed soil moisture data has a consistent ubRMSD compared with the original SMAP soil moisture data (0.04 m3/m3vs. 0.04 m3/m3). The PhyFill method can generate globally continuous, high accurate soil moisture estimates, providing remarkable support for advanced hydrological applications, e.g., global soil moisture dry-down events and patterns.

AN INTELLIGENT MODEL FOR BENIGN AND MALIGNANT PULMONARY NODULE ANALYSIS USING U-NET NETWORKS AND MULTILEVEL ATTENTION MECHANISMS

A key study area throughout the medical sector for image processing and analysis is medical image segmentation. The diagnosis and treatment strategies of doctors may have a solid foundation owing to accurate and effective medical image segmentation. Conventional approaches in this field rely on manual feature extraction, which makes segmentation complex, costs doctors’ time and energy, and involves a subjective evaluation that is readily susceptible to diagnostic errors. Researchers have applied convolutional neural network-based deep learning techniques to the segmentation of medical images as a result of their impressive advancements and successes in the field of computer vision. The research described here uses the U-Net network’s outstanding feature learning capabilities and end-to-end processing mode for lung CT image segmentation via fully convolutional network (FCN) research. However, focusing on valuable, crucial information aspects in the U-Net network is challenging. This study employed multilevel attention mechanisms on the basis of U-Net networks to enhance the model’s accuracy in lung CT image segmentation. These mechanisms were inspired by attention mechanisms. By improving the segmentation accuracy and optimizing the segmentation effect, the new model embeds a self-attention module in front of each upsampling layer in the U-Net model. This module provides more detailed information by stitching the self-attention module of the original image and then suppresses irrelevant and redundant information by using the effect of feature extraction of the upsampling layer. Several additional comparative experiments were conducted on the 2019nCoVR dataset. The outcomes demonstrate the efficacy of the optimized model described in this paper and its application results in improved segmentation effects in lung CT images. Additionally, the new model has distinct advantages over existing approaches that are typical of medical image segmentation, which represent its higher level of lung CT image segmentation.

Comprehensive Study on Plant Disease Detection by using Hybrid Convolution Techniques

Comprehensive Study on Plant Disease Detection by using Hybrid Convolution Techniques

Travel-mode inference based on GPS-trajectory data through multi-scale mixed attention mechanism

Identifying travel modes is essential for modern urban transportation planning and management. Recent advancements in data collection, especially those involving Global Positioning System (GPS) technology, offer promising opportunities for rapidly and accurately inferring users' travel modes. This study presents an innovative method for inferring travel modes from GPS trajectory data. The method utilizes multi-scale convolutional techniques to capture and analyze both temporal and spatial information of the data, thereby revealing the underlying spatiotemporal relationships inherent in user movement and behavior patterns. In addition, an attention mechanism is integrated into the model to enable autonomous learning. This mechanism enhances the model's capacity to identify and emphasize key information across different time periods and spatial locations, thus improving the accuracy of travel mode inference. Evaluation on the open-source GPS trajectory dataset, GeoLife, demonstrates that the proposed method attained an accuracy of 83.3%. This result highlights the effectiveness of the method, demonstrating that the model can more accurately understand and predict user travel modes through the integration of multi-scale convolutional technologies and attention mechanisms.

A brain tumor diagnosis approach based on deep separable convolutional networks and attention mechanisms

Brain tumors rank among the most lethal types of tumors. Magnetic Resonance Imaging (MRI) technology can clearly display the position, size, and borders of the tumors. Hence, MRI is frequently used in clinical diagnostics to detect brain tumors. Deep learning and related methods have been widely used in computer vision research recently for diagnosis and classification of MRI images of brain tumors. One trend is to achieve better performance by increasing model complexity. However, at the same time, the trainable parameters of the model increases accordingly. Too many parameters will lead to more difficult model training and optimization, and are prone to overfitting. To address this problem, this study constructs a model that incorporates a depthwise separable convolution technique and an attention mechanism to balance model performance and complexity. The model achieves 97.41% accuracy in the brain tumor classification task, which exceeds the 96.72% accuracy of the pre-trained model MobileNetV2, and shows good image classification ability. Future work could test the model's classification results on noisy images and investigate how to optimize the model's generalization ability.

A brain tumor diagnosis approach based on deep separable convolutional networks and attention mechanisms

Brain tumors rank among the most lethal types of tumors. Magnetic Resonance Imaging (MRI) technology can clearly display the position, size, and borders of the tumors. Hence, MRI is frequently used in clinical diagnostics to detect brain tumors. Deep learning and related methods have been widely used in computer vision research recently for diagnosis and classification of MRI images of brain tumors. One trend is to achieve better performance by increasing model complexity. However, at the same time, the trainable parameters of the model increases accordingly. Too many parameters will lead to more difficult model training and optimization, and are prone to overfitting. To address this problem, this study constructs a model that incorporates a depthwise separable convolution technique and an attention mechanism to balance model performance and complexity. The model achieves 97.41% accuracy in the brain tumor classification task, which exceeds the 96.72% accuracy of the pre-trained model MobileNetV2, and shows good image classification ability. Future work could test the model's classification results on noisy images and investigate how to optimize the model's generalization ability.

PUDCN: two-dimensional phase unwrapping with a deformable convolutional network

Two-dimensional phase unwrapping is a fundamental yet vital task in optical imaging and measurement. In this paper, what we believe to be a novel deep learning framework PUDCN is proposed for 2D phase unwrapping. We introduce the deformable convolution technique in the PUDCN and design two deformable convolution-related plugins for dynamic feature extraction. In addition, PUDCN adopts a coarse-to-fine strategy that unwraps the phase in the first stage and then refines the unwrapped phase in the second stage to obtain an accurate result. The experiments show that our PUDCN performs better than the existing state-of-the-art. Furthermore, we apply PUDCN to unwrap the phase of optical fibers in optical interferometry, demonstrating its generalization ability.

Water body extraction from high spatial resolution remote sensing images based on enhanced U-Net and multi-scale information fusion

Employing deep learning techniques for the semantic segmentation of remote sensing images has emerged as a prevalent approach for acquiring information about water bodies. Yet, current models frequently fall short in accurately extracting water bodies from high-resolution remote sensing images, as these images often present intricate details of terrestrial objects and complex backgrounds. Vegetation, shadows, and other objects close to water boundaries have increased similarity to water bodies. Moreover, water bodies in high-resolution images have different boundary complexities, shapes, and sizes. This situation makes it somewhat challenging to accurately distinguish water bodies in high-resolution images. To overcome these difficulties, this paper presents a novel network model named EU-Net, specifically designed to extract water bodies from high-resolution remote sensing images. The proposed EU-Net model, with U-net as the backbone network, incorporates improved residual connections and attention mechanisms, and designs multi-scale dilated convolution and multi-scale feature fusion modules to enhance water body extraction performance in complex scenarios. Specifically, in the proposed model, improved residual connections are introduced to enable the learning of more complex features; the attention mechanism is employed to improve the model's discriminative ability by focusing on important channels and spatial areas. The implemented multi-scale dilated convolution technique enhances the model's receptive field while maintaining the same number of parameters. The designed multi-scale feature fusion module is capable of processing both small-scale details and large-scale structures in images, while simultaneously modeling the spatial context relationships of features at different scales. Experimental results validate the superior performance of EU-Net in accurately identifying water bodies from high-resolution remote sensing images, outperforming current models in terms of water extraction accuracy.