Cascading multi-segment rupture process of the 2023 Turkish earthquake doublet on a complex fault system revealed by teleseismic P wave back projection method

In this study,the vertical components of broadband teleseismic P wave data recorded by China Earthquake Network are used to image the rupture processes of the February 6th,2023 Turkish earthquake doublet via back projection analysis.Data in two frequency bands(0.5-2 Hz and 1-3 Hz)are used in the imaging processes.The results show that the rupture of the first event extends about 200 km to the northeast and about 150 km to the southwest,lasting~90 s in total.The southwestern rupture is triggered by the northeastern rupture,demonstrating a sequential bidirectional unilateral rupture pattern.The rupture of the second event extends approximately 80 km in both northeast and west directions,lasting~35 s in total and demonstrates a typical bilateral rupture feature.The cascading ruptures on both sides also reflect the occurrence of selective rupture behaviors on bifurcated faults.In addition,we observe super-shear ruptures on certain fault sections with relatively straight fault structures and sparse aftershocks.

UNet Based onMulti-Object Segmentation and Convolution Neural Network for Object Recognition

The recent advancements in vision technology have had a significant impact on our ability to identify multiple objects and understand complex scenes.Various technologies,such as augmented reality-driven scene integration,robotic navigation,autonomous driving,and guided tour systems,heavily rely on this type of scene comprehension.This paper presents a novel segmentation approach based on the UNet network model,aimed at recognizing multiple objects within an image.The methodology begins with the acquisition and preprocessing of the image,followed by segmentation using the fine-tuned UNet architecture.Afterward,we use an annotation tool to accurately label the segmented regions.Upon labeling,significant features are extracted from these segmented objects,encompassing KAZE(Accelerated Segmentation and Extraction)features,energy-based edge detection,frequency-based,and blob characteristics.For the classification stage,a convolution neural network(CNN)is employed.This comprehensive methodology demonstrates a robust framework for achieving accurate and efficient recognition of multiple objects in images.The experimental results,which include complex object datasets like MSRC-v2 and PASCAL-VOC12,have been documented.After analyzing the experimental results,it was found that the PASCAL-VOC12 dataset achieved an accuracy rate of 95%,while the MSRC-v2 dataset achieved an accuracy of 89%.The evaluation performed on these diverse datasets highlights a notably impressive level of performance.

Multi-Level Parallel Network for Brain Tumor Segmentation

Accurate automatic segmentation of gliomas in various sub-regions,including peritumoral edema,necrotic core,and enhancing and non-enhancing tumor core from 3D multimodal MRI images,is challenging because of its highly heterogeneous appearance and shape.Deep convolution neural networks(CNNs)have recently improved glioma segmentation performance.However,extensive down-sampling such as pooling or stridden convolution in CNNs significantly decreases the initial image resolution,resulting in the loss of accurate spatial and object parts information,especially information on the small sub-region tumors,affecting segmentation performance.Hence,this paper proposes a novel multi-level parallel network comprising three different level parallel subnetworks to fully use low-level,mid-level,and high-level information and improve the performance of brain tumor segmentation.We also introduce the Combo loss function to address input class imbalance and false positives and negatives imbalance in deep learning.The proposed method is trained and validated on the BraTS 2020 training and validation dataset.On the validation dataset,ourmethod achieved a mean Dice score of 0.907,0.830,and 0.787 for the whole tumor,tumor core,and enhancing tumor core,respectively.Compared with state-of-the-art methods,the multi-level parallel network has achieved competitive results on the validation dataset.

MCIF-Transformer Mask RCNN:Multi-Branch Cross-Scale Interactive Feature Fusion Transformer Model for PET/CT Lung Tumor Instance Segmentation

The precise detection and segmentation of tumor lesions are very important for lung cancer computer-aided diagnosis.However,in PET/CT(Positron Emission Tomography/Computed Tomography)lung images,the lesion shapes are complex,the edges are blurred,and the sample numbers are unbalanced.To solve these problems,this paper proposes a Multi-branch Cross-scale Interactive Feature fusion Transformer model(MCIF-Transformer Mask RCNN)for PET/CT lung tumor instance segmentation,The main innovative works of this paper are as follows:Firstly,the ResNet-Transformer backbone network is used to extract global feature and local feature in lung images.The pixel dependence relationship is established in local and non-local fields to improve the model perception ability.Secondly,the Cross-scale Interactive Feature Enhancement auxiliary network is designed to provide the shallow features to the deep features,and the cross-scale interactive feature enhancement module(CIFEM)is used to enhance the attention ability of the fine-grained features.Thirdly,the Cross-scale Interactive Feature fusion FPN network(CIF-FPN)is constructed to realize bidirectional interactive fusion between deep features and shallow features,and the low-level features are enhanced in deep semantic features.Finally,4 ablation experiments,3 comparison experiments of detection,3 comparison experiments of segmentation and 6 comparison experiments with two-stage and single-stage instance segmentation networks are done on PET/CT lung medical image datasets.The results showed that APdet,APseg,ARdet and ARseg indexes are improved by 5.5%,5.15%,3.11%and 6.79%compared with Mask RCNN(resnet50).Based on the above research,the precise detection and segmentation of the lesion region are realized in this paper.This method has positive significance for the detection of lung tumors.

Efficient Object Segmentation and Recognition Using Multi-Layer Perceptron Networks

Object segmentation and recognition is an imperative area of computer vision andmachine learning that identifies and separates individual objects within an image or video and determines classes or categories based on their features.The proposed system presents a distinctive approach to object segmentation and recognition using Artificial Neural Networks(ANNs).The system takes RGB images as input and uses a k-means clustering-based segmentation technique to fragment the intended parts of the images into different regions and label thembased on their characteristics.Then,two distinct kinds of features are obtained from the segmented images to help identify the objects of interest.An Artificial Neural Network(ANN)is then used to recognize the objects based on their features.Experiments were carried out with three standard datasets,MSRC,MS COCO,and Caltech 101 which are extensively used in object recognition research,to measure the productivity of the suggested approach.The findings from the experiment support the suggested system’s validity,as it achieved class recognition accuracies of 89%,83%,and 90.30% on the MSRC,MS COCO,and Caltech 101 datasets,respectively.

Multi-Level Image Segmentation Combining Chaotic Initialized Chimp Optimization Algorithm and Cauchy Mutation

To enhance the diversity and distribution uniformity of initial population,as well as to avoid local extrema in the Chimp Optimization Algorithm(CHOA),this paper improves the CHOA based on chaos initialization and Cauchy mutation.First,Sin chaos is introduced to improve the random population initialization scheme of the CHOA,which not only guarantees the diversity of the population,but also enhances the distribution uniformity of the initial population.Next,Cauchy mutation is added to optimize the global search ability of the CHOA in the process of position(threshold)updating to avoid the CHOA falling into local optima.Finally,an improved CHOA was formed through the combination of chaos initialization and Cauchy mutation(CICMCHOA),then taking fuzzy Kapur as the objective function,this paper applied CICMCHOA to natural and medical image segmentation,and compared it with four algorithms,including the improved Satin Bowerbird optimizer(ISBO),Cuckoo Search(ICS),etc.The experimental results deriving from visual and specific indicators demonstrate that CICMCHOA delivers superior segmentation effects in image segmentation.

Multi-Scale Mixed Attention Tea Shoot Instance Segmentation Model

Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.

Two Stages Segmentation Algorithm of Breast Tumor in DCE-MRI Based on Multi-Scale Feature and Boundary Attention Mechanism

Nuclearmagnetic resonance imaging of breasts often presents complex backgrounds.Breast tumors exhibit varying sizes,uneven intensity,and indistinct boundaries.These characteristics can lead to challenges such as low accuracy and incorrect segmentation during tumor segmentation.Thus,we propose a two-stage breast tumor segmentation method leveraging multi-scale features and boundary attention mechanisms.Initially,the breast region of interest is extracted to isolate the breast area from surrounding tissues and organs.Subsequently,we devise a fusion network incorporatingmulti-scale features and boundary attentionmechanisms for breast tumor segmentation.We incorporate multi-scale parallel dilated convolution modules into the network,enhancing its capability to segment tumors of various sizes through multi-scale convolution and novel fusion techniques.Additionally,attention and boundary detection modules are included to augment the network’s capacity to locate tumors by capturing nonlocal dependencies in both spatial and channel domains.Furthermore,a hybrid loss function with boundary weight is employed to address sample class imbalance issues and enhance the network’s boundary maintenance capability through additional loss.Themethod was evaluated using breast data from 207 patients at RuijinHospital,resulting in a 6.64%increase in Dice similarity coefficient compared to the benchmarkU-Net.Experimental results demonstrate the superiority of the method over other segmentation techniques,with fewer model parameters.

Multi-task Learning of Semantic Segmentation and Height Estimation for Multi-modal Remote Sensing Images

Deep learning based methods have been successfully applied to semantic segmentation of optical remote sensing images.However,as more and more remote sensing data is available,it is a new challenge to comprehensively utilize multi-modal remote sensing data to break through the performance bottleneck of single-modal interpretation.In addition,semantic segmentation and height estimation in remote sensing data are two tasks with strong correlation,but existing methods usually study individual tasks separately,which leads to high computational resource overhead.To this end,we propose a Multi-Task learning framework for Multi-Modal remote sensing images(MM_MT).Specifically,we design a Cross-Modal Feature Fusion(CMFF)method,which aggregates complementary information of different modalities to improve the accuracy of semantic segmentation and height estimation.Besides,a dual-stream multi-task learning method is introduced for Joint Semantic Segmentation and Height Estimation(JSSHE),extracting common features in a shared network to save time and resources,and then learning task-specific features in two task branches.Experimental results on the public multi-modal remote sensing image dataset Potsdam show that compared to training two tasks independently,multi-task learning saves 20%of training time and achieves competitive performance with mIoU of 83.02%for semantic segmentation and accuracy of 95.26%for height estimation.

Intelligent Traffic Surveillance through Multi-Label Semantic Segmentation and Filter-Based Tracking

Road congestion,air pollution,and accident rates have all increased as a result of rising traffic density andworldwide population growth.Over the past ten years,the total number of automobiles has increased significantly over the world.In this paper,a novel method for intelligent traffic surveillance is presented.The proposed model is based on multilabel semantic segmentation using a random forest classifier which classifies the images into five classes.To improve the results,mean-shift clustering was applied to the segmented images.Afterward,the pixels given the label for the vehicle were extracted and blob detection was applied to mark each vehicle.For the validation of each detection,a vehicle verification method based on the structural similarity index is proposed.The tracking of vehicles across the image frames is done using the Identifier(ID)assignment technique and particle filter.Also,vehicle counting in each frame along with trajectory estimation was done for each object.Our proposed system demonstrated a remarkable vehicle detection rate of 0.83 over Vehicle Aerial Imaging from Drone(VAID),0.86 over AU-AIR,and 0.75 over the Unmanned Aerial Vehicle Benchmark Object Detection and Tracking(UAVDT)dataset during the experimental evaluation.The proposed system can be used for several purposes,such as vehicle identification in traffic,traffic density estimation at intersections,and traffic congestion sensing on a road.

Liver Tumor Segmentation Based on Multi-Scale and Self-Attention Mechanism

Liver cancer has the second highest incidence rate among all types of malignant tumors,and currently,its diagnosis heavily depends on doctors’manual labeling of CT scan images,a process that is time-consuming and susceptible to subjective errors.To address the aforementioned issues,we propose an automatic segmentation model for liver and tumors called Res2Swin Unet,which is based on the Unet architecture.The model combines Attention-Res2 and Swin Transformer modules for liver and tumor segmentation,respectively.Attention-Res2 merges multiple feature map parts with an Attention gate via skip connections,while Swin Transformer captures long-range dependencies and models the input globally.And the model uses deep supervision and a hybrid loss function for faster convergence.On the LiTS2017 dataset,it achieves better segmentation performance than other models,with an average Dice coefficient of 97.0%for liver segmentation and 81.2%for tumor segmentation.

Visual Semantic Segmentation Based on Few/Zero-Shot Learning:An Overview

Visual semantic segmentation aims at separating a visual sample into diverse blocks with specific semantic attributes and identifying the category for each block,and it plays a crucial role in environmental perception.Conventional learning-based visual semantic segmentation approaches count heavily on largescale training data with dense annotations and consistently fail to estimate accurate semantic labels for unseen categories.This obstruction spurs a craze for studying visual semantic segmentation with the assistance of few/zero-shot learning.The emergence and rapid progress of few/zero-shot visual semantic segmentation make it possible to learn unseen categories from a few labeled or even zero-labeled samples,which advances the extension to practical applications.Therefore,this paper focuses on the recently published few/zero-shot visual semantic segmentation methods varying from 2D to 3D space and explores the commonalities and discrepancies of technical settlements under different segmentation circumstances.Specifically,the preliminaries on few/zeroshot visual semantic segmentation,including the problem definitions,typical datasets,and technical remedies,are briefly reviewed and discussed.Moreover,three typical instantiations are involved to uncover the interactions of few/zero-shot learning with visual semantic segmentation,including image semantic segmentation,video object segmentation,and 3D segmentation.Finally,the future challenges of few/zero-shot visual semantic segmentation are discussed.

基于Multi-WHFPN与SimAM注意力机制的版面分割

作为OCR的预处理工作,版面分割技术越来越受到学术界和工业界重视。针对版面分割中遇到的检测速度慢、目标区域边界不准确以及细小目标易遗漏等问题,提出了YOLOv7-MSY模型。此模型首先借鉴残差连接思想,提出了Multi-WHFPN网络结构。它采用可训练的权重参数,突出特征融合过程中特征重要性,并添加了小目标检测头,从而提升对小目标的检测性能;其次,引入SimAM注意力机制,可以在不增加额外参数的基础上在3D维度评估特征权重,以增强重要特征,抑制无效特征;最后,使用YEIOU来代替原模型中的定位损失函数,提升了模型的收敛速度与回归精度。在江苏省档案馆提供的数据集上进行实验对比,YOLOv7-MSY对目标区域边界检测更加敏感,对细小目标的检测效果更好。YOLOv7-MSY的mAP@.5达到了0.871,相较于原YOLOv7模型提高了7.84%。该模型的版面分割的效果优于其他类型的版面分割算法,具有良好的泛化性能,并且版面分割速度处于较高水平。

Multilevel Attention Unet Segmentation Algorithmfor Lung Cancer Based on CT Images

Lung cancer is a malady of the lungs that gravely jeopardizes human health.Therefore,early detection and treatment are paramount for the preservation of human life.Lung computed tomography(CT)image sequences can explicitly delineate the pathological condition of the lungs.To meet the imperative for accurate diagnosis by physicians,expeditious segmentation of the region harboring lung cancer is of utmost significance.We utilize computer-aided methods to emulate the diagnostic process in which physicians concentrate on lung cancer in a sequential manner,erect an interpretable model,and attain segmentation of lung cancer.The specific advancements can be encapsulated as follows:1)Concentration on the lung parenchyma region:Based on 16-bit CT image capturing and the luminance characteristics of lung cancer,we proffer an intercept histogram algorithm.2)Focus on the specific locus of lung malignancy:Utilizing the spatial interrelation of lung cancer,we propose a memory-based Unet architecture and incorporate skip connections.3)Data Imbalance:In accordance with the prevalent situation of an overabundance of negative samples and a paucity of positive samples,we scrutinize the existing loss function and suggest a mixed loss function.Experimental results with pre-existing publicly available datasets and assembled datasets demonstrate that the segmentation efficacy,measured as Area Overlap Measure(AOM)is superior to 0.81,which markedly ameliorates in comparison with conventional algorithms,thereby facilitating physicians in diagnosis.

Two-Staged Method for Ice Channel Identification Based on Image Segmentation and Corner Point Regression

Identification of the ice channel is the basic technology for developing intelligent ships in ice-covered waters,which is important to ensure the safety and economy of navigation.In the Arctic,merchant ships with low ice class often navigate in channels opened up by icebreakers.Navigation in the ice channel often depends on good maneuverability skills and abundant experience from the captain to a large extent.The ship may get stuck if steered into ice fields off the channel.Under this circumstance,it is very important to study how to identify the boundary lines of ice channels with a reliable method.In this paper,a two-staged ice channel identification method is developed based on image segmentation and corner point regression.The first stage employs the image segmentation method to extract channel regions.In the second stage,an intelligent corner regression network is proposed to extract the channel boundary lines from the channel region.A non-intelligent angle-based filtering and clustering method is proposed and compared with corner point regression network.The training and evaluation of the segmentation method and corner regression network are carried out on the synthetic and real ice channel dataset.The evaluation results show that the accuracy of the method using the corner point regression network in the second stage is achieved as high as 73.33%on the synthetic ice channel dataset and 70.66%on the real ice channel dataset,and the processing speed can reach up to 14.58frames per second.

Micro segment analysis of supercritical methane thermal-hydraulic performance and pseudo-boiling in a PCHE straight channel

The printed circuit heat exchanger(PCHE) is receiving wide attention as a new kind of compact heat exchanger and is considered as a promising vaporizer in the LNG process. In this paper, a PCHE straight channel in the length of 500 mm is established, with a semicircular cross section in a diameter of 1.2 mm.Numerical simulation is employed to investigate the flow and heat transfer performance of supercritical methane in the channel. The pseudo-boiling theory is adopted and the liquid-like, two-phase-like, and vapor-like regimes are divided for supercritical methane to analyze the heat transfer and flow features.The results are presented in micro segment to show the local convective heat transfer coefficient and pressure drop. It shows that the convective heat transfer coefficient in segments along the channel has a significant peak feature near the pseudo-critical point and a heat transfer deterioration when the average fluid temperature in the segment is higher than the pseudo-critical point. The reason is explained with the generation of vapor-like film near the channel wall that the peak feature related to a nucleateboiling-like state and heat transfer deterioration related to a film-boiling-like state. The effects of parameters, including mass flow rate, pressure, and wall heat flux on flow and heat transfer were analyzed.In calculating of the averaged heat transfer coefficient of the whole channel, the traditional method shows significant deviation and the micro segment weighted average method is adopted. The pressure drop can mainly be affected by the mass flux and pressure and little affected by the wall heat flux. The peak of the convective heat transfer coefficient can only form at high mass flux, low wall heat flux, and near critical pressure, in which condition the nucleate-boiling-like state is easier to appear. Moreover,heat transfer deterioration will always appear, since the supercritical flow will finally develop into a filmboiling-like state. So heat transfer deterioration should be taken seriously in the design and safe operation of vaporizer PCHE. The study of this work clarified the local heat transfer and flow feature of supercritical methane in microchannel and contributed to the deep understanding of supercritical methane flow of the vaporization process in PCHE.

Crack Segmentation Based on Fusing Multi-Scale Wavelet and Spatial-Channel Attention

Accurate and reliable crack segmentation is a challenge and meaningful task.In this article,aiming at the characteristics of cracks on the concrete images,the intensity frequency information of source images which is obtained by Discrete Wavelet Transform(DWT)is fed into deep learning-based networks to enhance the ability of network on crack segmentation.To well integrate frequency information into network an effective and novel DWTA module based on the DWT and scSE attention mechanism is proposed.The semantic information of cracks is enhanced and the irrelevant information is suppressed by DWTA module.And the gap between frequency information and convolution information from network is balanced by DWTA module which can well fuse wavelet information into image segmentation network.The Unet-DWTA is proposed to preserved the information of crack boundary and thin crack in intermediate feature maps by adding DWTA module in the encoderdecoder structures.In decoder,diverse level feature maps are fused to capture the information of crack boundary and the abstract semantic information which is beneficial to crack pixel classification.The proposed method is verified on three classic datasets including CrackDataset,CrackForest,and DeepCrack datasets.Compared with the other crack methods,the proposed Unet-DWTA shows better performance based on the evaluation of the subjective analysis and objective metrics about image semantic segmentation.

Improved organs at risk segmentation based on modified U‐Net with self‐attention and consistency regularisation

Cancer is one of the leading causes of death in the world,with radiotherapy as one of the treatment options.Radiotherapy planning starts with delineating the affected area from healthy organs,called organs at risk(OAR).A new approach to automatic OAR seg-mentation in the chest cavity in Computed Tomography(CT)images is presented.The proposed approach is based on the modified U‐Net architecture with the ResNet‐34 encoder,which is the baseline adopted in this work.The new two‐branch CS‐SA U‐Net architecture is proposed,which consists of two parallel U‐Net models in which self‐attention blocks with cosine similarity as query‐key similarity function(CS‐SA)blocks are inserted between the encoder and decoder,which enabled the use of con-sistency regularisation.The proposed solution demonstrates state‐of‐the‐art performance for the problem of OAR segmentation in CT images on the publicly available SegTHOR benchmark dataset in terms of a Dice coefficient(oesophagus-0.8714,heart-0.9516,trachea-0.9286,aorta-0.9510)and Hausdorff distance(oesophagus-0.2541,heart-0.1514,trachea-0.1722,aorta-0.1114)and significantly outperforms the baseline.The current approach is demonstrated to be viable for improving the quality of OAR segmentation for radiotherapy planning.

Empowering Diagnosis: Cutting-Edge Segmentation and Classification in Lung Cancer Analysis

Lung cancer is a leading cause of global mortality rates.Early detection of pulmonary tumors can significantly enhance the survival rate of patients.Recently,various Computer-Aided Diagnostic(CAD)methods have been developed to enhance the detection of pulmonary nodules with high accuracy.Nevertheless,the existing method-ologies cannot obtain a high level of specificity and sensitivity.The present study introduces a novel model for Lung Cancer Segmentation and Classification(LCSC),which incorporates two improved architectures,namely the improved U-Net architecture and the improved AlexNet architecture.The LCSC model comprises two distinct stages.The first stage involves the utilization of an improved U-Net architecture to segment candidate nodules extracted from the lung lobes.Subsequently,an improved AlexNet architecture is employed to classify lung cancer.During the first stage,the proposed model demonstrates a dice accuracy of 0.855,a precision of 0.933,and a recall of 0.789 for the segmentation of candidate nodules.The suggested improved AlexNet architecture attains 97.06%accuracy,a true positive rate of 96.36%,a true negative rate of 97.77%,a positive predictive value of 97.74%,and a negative predictive value of 96.41%for classifying pulmonary cancer as either benign or malignant.The proposed LCSC model is tested and evaluated employing the publically available dataset furnished by the Lung Image Database Consortium and Image Database Resource Initiative(LIDC-IDRI).This proposed technique exhibits remarkable performance compared to the existing methods by using various evaluation parameters.

CrossFormer Embedding DeepLabv3+ for Remote Sensing Images Semantic Segmentation

High-resolution remote sensing image segmentation is a challenging task. In urban remote sensing, the presenceof occlusions and shadows often results in blurred or invisible object boundaries, thereby increasing the difficultyof segmentation. In this paper, an improved network with a cross-region self-attention mechanism for multi-scalefeatures based onDeepLabv3+is designed to address the difficulties of small object segmentation and blurred targetedge segmentation. First,we use CrossFormer as the backbone feature extraction network to achieve the interactionbetween large- and small-scale features, and establish self-attention associations between features at both large andsmall scales to capture global contextual feature information. Next, an improved atrous spatial pyramid poolingmodule is introduced to establish multi-scale feature maps with large- and small-scale feature associations, andattention vectors are added in the channel direction to enable adaptive adjustment of multi-scale channel features.The proposed networkmodel is validated using the PotsdamandVaihingen datasets. The experimental results showthat, compared with existing techniques, the network model designed in this paper can extract and fuse multiscaleinformation, more clearly extract edge information and small-scale information, and segment boundariesmore smoothly. Experimental results on public datasets demonstrate the superiority of ourmethod compared withseveral state-of-the-art networks.