Fully Convolutional Network Research Articles

Faulty-Feeder Detection for Single Phase-to-Ground Faults in Distribution Networks Based on Waveform Encoding and Waveform Segmentation

Faulty feeder detection helps ensure the stability and safety of power grids after single-phase-to-ground (SPG) faults occur in distribution networks. The existing detection techniques identify the faulty feeder by extracting representative fault features, while they fail to show reliable detection performance due to variable fault conditions and complex fault transients. To address these drawbacks, this paper proposes a method based on waveform encoding and waveform segmentation. Since the waveforms have complete fault features in fault signals, it is suitable to recognize the signals on the waveform scale, rather than extracting and fusing several fault features. Firstly, raw sampled zero-sequence voltage (ZSV) and zero-sequence current (ZSC) are processed by using the proposed encoding method, and the ZSV-ZSC image can be generated quickly. Secondly, to learn and understand the waveforms of ZSV and ZSC, a two-path fully convolutional network (FCN) is established to make pixel-wise prediction on the ZSV-ZSC image. Finally, the fault degree of each feeder can be estimated based on the segmented waveform in the ZSV-ZSC image. The performance evaluation is implemented in the NVIDIA Jetson Xavier embedded platform, and the experimental results demonstrate that the proposed method can identify the faulty feeder with high accuracy within 28 ms.

Crack segmentation in the wild using convolutional neural networks and bootstrapping

AbstractComputer vision and deep learning methods have found numerous practical applications over the last decade. The field of structural health monitoring has greatly benefited from such advancements. Accurate crack detection and segmentation are a critical part of structural health monitoring and assessment. In the past decade, researchers have developed different computer vision and deep learning methods to address this challenging task. In this article, we propose to use convolutional neural networks and bootstrapping to improve crack segmentation in the wild. Specifically, we investigate fully convolutional network (FCN) and DeepLabV3 with different ResNet architectures as the back bone and assess their performance. A unique feature of this work is the use of bootstrapping with a segmentation network and image augmentation. Bootstrapping is an important component of our proposed methodology for better extracting features from the datasets. We also incorporate superpixel pooling for FCN which improves performance. We assess the performance of our method using five publicly available datasets, which include a wide variety of crack images such as thin pavement cracks, tree shadows on cracks, cracks in asphalt roads, concrete wall cracks, wide cracks on concrete surfaces, and so on. A comparative study is also performed with several established methods in the literature. Results indicate that overall, our proposed method outperforms other state‐of‐the‐art methods for crack segmentation.

Water index and Swin Transformer Ensemble (WISTE) for water body extraction from multispectral remote sensing images

ABSTRACT Automatic surface water body mapping using remote sensing technology is greatly meaningful for studying inland water dynamics at regional to global scales. Convolutional neural networks (CNN) have become an efficient semantic segmentation technique for the interpretation of remote sensing images. However, the receptive field value of a CNN is restricted by the convolutional kernel size because the network only focuses on local features. The Swin Transformer has recently demonstrated its outstanding performance in computer vision tasks, and it could be useful for processing multispectral remote sensing images. In this article, a Water Index and Swin Transformer Ensemble (WISTE) method for automatic water body extraction is proposed. First, a dual-branch encoder architecture is designed for the Swin Transformer, aggregating the global semantic information and pixel neighbor relationships captured by fully convolutional networks (FCN) and multihead self-attention. Second, to prevent the Swin Transformer from ignoring multispectral information, we construct a prediction map ensemble module. The predictions of the Swin Transformer and the Normalized Difference Water Index (NDWI) are combined by a Bayesian averaging strategy. Finally, the experimental results obtained on two distinct datasets demonstrate that the WISTE has advantages over other segmentation methods and achieves the best results. The method proposed in this study can be used for improving regional to continental surface water mapping and related hydrological studies.

Data anomaly detection with automatic feature selection and deep learning

As increasing sensor-based structural health monitoring (SHM) systems are implemented on civil infrastructures, sensor data reliability plays a crucial role in the assessment of operational performance of bridges. Sensors are heavily exposed to harsh environmental conditions during their operations and inevitably lead to possible unstable performance or failure. Thus, to accurately identify faulty sensors is a prerequisite to processing and analyzing the collected data for assessment purpose. Recently, researchers adopted the convolutional neural network (CNN) approach to identify faulty sensors, focusing on image features. Such approach may overlook some important detailed signal features and the time series approach may still be needed. However, algorithms based on time series tend to be time consuming because of the lengthy and high dimensional dataset. This may be effectively resolved using an automatic feature selection technique, namely Tsfresh, as proposed in this paper to select highly relevant signal features based on statistical tests of significance. A deep learning technique based on fully convolutional network (FCN) can then be efficiently employed for anomaly classification. The algorithm is validated using a dataset collected from a real cable-stayed bridge and results show that the proposed method significantly reduces the training time for the neural network, albeit with high classification accuracy.

Optimization of U-shaped pure transformer medical image segmentation network.

In recent years, neural networks have made pioneering achievements in the field of medical imaging. In particular, deep neural networks based on U-shaped structures are widely used in different medical image segmentation tasks. In order to improve the early diagnosis and clinical decision-making system of lung diseases, it has become a key step to use the neural network for lung segmentation to assist in positioning and observing the shape. There is still the problem of low precision. For the sake of achieving better segmentation accuracy, an optimized pure Transformer U-shaped segmentation is proposed in this article. The optimization segmentation network adopts the method of adding skip connections and performing special splicing processing, which reduces the information loss in the encoding process and increases the information in the decoding process, so as to achieve the purpose of improving the segmentation accuracy. The final experiment shows that our improved network achieves 97.86% accuracy in segmentation of the "Chest Xray Masks and Labels" dataset, which is better than the full convolutional network or the combination of Transformer and convolution.

Mapping Irrigated Croplands from Sentinel-2 Images Using Deep Convolutional Neural Networks

Understanding the spatial distribution of irrigated croplands is crucial for food security and water use. To map land cover classes with high-spatial-resolution images, it is necessary to analyze the semantic information of target objects in addition to the spectral or spatial–spectral information of local pixels. Deep convolutional neural networks (DCNNs) can characterize the semantic features of objects adaptively. This study uses DCNNs to extract irrigated croplands from Sentinel-2 images in the states of Washington and California in the United States. We integrated the DCNNs of 101 layers, discarded pooling layers, and employed dilation convolution to preserve location information; these are models which were used based on fully convolutional network (FCN) architectures. The findings indicated that irrigated croplands may be effectively detected at various phases of crop growth in the fields. A quantitative analysis of the trained models revealed that the three models in the two states had the lowest values of Intersection over Union (IoU) and Kappa, i.e., 0.88 and 0.91, respectively. The deep models’ temporal portability across different years was acceptable. The lowest values of recall and OA (overall accuracy) from 2018 to 2021 were 0.91 and 0.87, respectively. In Washington, the lowest OA value from 10 to 300 m resolution was 0.76. This study demonstrates the potential of FCNs + DCNNs approaches for mapping irrigated croplands across large regions, providing a solution for irrigation mapping. The spatial resolution portability of deep models could be improved further by designing model architectures.

FGRC-Net: A high-information interactive convolutional neural network for identifying ink spectral information

Modifying some keywords or numbers on documents to change the original intention is illegal. In some litigation cases, especially economic cases, there is often a need to examine the type of ink on documents. This paper proposes a nondestructive and accurate detection method for ink inspection. In this work, a high-information interactive, full group residual convolution network (FGRC-Net) is proposed and combined with spectral information to classify the inks of six different brands of pens. First, FGRC-Net realizes the information interaction among channels to effectively improve the feature extraction ability through multigroup convolution and multipath cascade. Meanwhile, to avoid feature degradation, the residual connection is introduced. Second, the ink spectral information of six pens based on the hyperspectral system is obtained. Finally, in comparing FGRC-Net and other deep learning methods, FGRC-Net shows the best classification performance with 98.38% accuracy, 98.53% precision, and 98.40% recall. The results show that combining FGRC-Net with a hyperspectral system is an effective detection method for handwritten ink classification. It also provides an effective detection method for handwritten document ink inspection. At the same time, the universality and validity of FGRC-Net in processing spectral information are verified by the comparison of multiple datasets.

Modified rat swarm optimization with deep learning model for robust recycling object detection and classification

Biomass residues consist of sewage effluents and sludges, crop wastes, non-recyclable municipal solid waste industrial and domestic greywater, and much more. Recycling is regarded as one such significant disposal technique. Considering the intelligent classification and detection of solid waste as an essential factor in consumption and recycling, multi-object solid waste identification and classification methodology on the basis of transfer learning (TL) are presented. This study develops a Modified Rat Swarm Optimization with Deep Learning for Robust Recycling Object Detection and Classification (MRSODL-RODC) model. Primarily, fully convolutional network (FCN) is applied for the identification of waste objects. Moreover, MRSO with deep belief network (DBN) model is applied for object detection process. The design of the MRSO algorithm for the DBN technique, showing the novelty of our work. The performance validation of the MRSODL-RODC model can be executed with the help of the benchmark dataset from Kaggle repository. The experimental outcomes demonstrated the better performance of the MRSODL-RODC model over recent approaches with higher accuracy of 99.20%.

Stochastic gradient descent optimisation for convolutional neural network for medical image segmentation.

In accordance with the inability of various hair artefacts subjected to dermoscopic medical images, undergoing illumination challenges that include chest-Xray featuring conditions of imaging acquisi-tion situations built with clinical segmentation. The study proposed a novel deep-convolutional neural network (CNN)-integrated methodology for applying medical image segmentation upon chest-Xray and dermoscopic clinical images. The study develops a novel technique of segmenting medical images merged with CNNs with an architectural comparison that incorporates neural networks of U-net and fully convolutional networks (FCN) schemas with loss functions associated with Jaccard distance and Binary-cross entropy under optimised stochastic gradient descent + Nesterov practices. Digital image over clinical approach significantly built the diagnosis and determination of the best treatment for a patient's condition. Even though medical digital images are subjected to varied components clarified with the effect of noise, quality, disturbance, and precision depending on the enhanced version of images segmented with the optimised process. Ultimately, the threshold technique has been employed for the output reached under the pre- and post-processing stages to contrast the image technically being developed. The data source applied is well-known in PH2 Database for Melanoma lesion segmentation and chest X-ray images since it has variations in hair artefacts and illumination. Experiment outcomes outperform other U-net and FCN architectures of CNNs. The predictions produced from the model on test images were post-processed using the threshold technique to remove the blurry boundaries around the predicted lesions. Experimental results proved that the present model has better efficiency than the existing one, such as U-net and FCN, based on the image segmented in terms of sensitivity = 0.9913, accuracy = 0.9883, and dice coefficient = 0.0246.

BiFTransNet: A unified and simultaneous segmentation network for gastrointestinal images of CT & MRI

Gastrointestinal (GI) cancer is a malignancy affecting the digestive organs. During radiation therapy, the radiation oncologist must precisely aim the X-ray beam at the tumor while avoiding unaffected areas of the stomach and intestines. Consequently, accurate, automated GI image segmentation is urgently needed in clinical practice. While the fully convolutional network (FCN) and U-Net framework have shown impressive results in medical image segmentation, their ability to model long-range dependencies is constrained by the convolutional kernel’s restricted receptive field. The transformer has a robust capacity for global modeling owing to its inherent global self-attention mechanism. The TransUnet model leverages the strengths of both the convolutional neural network (CNN) and transformer models through a hybrid CNN-transformer encoder. However, the concatenation of high- and low-level features in the decoder is ineffective in fusing global and local information. To overcome this limitation, we propose an innovative transformer-based medical image segmentation architecture called BiFTransNet, which introduces a BiFusion module into the decoder stage, enabling effective global and local feature fusion by enabling feature integration from various modules. Further, a multilevel loss (ML) strategy is introduced to oversee the learning process of each decoder layer and optimize the use of globally and locally fused contextual features at different scales. Our method achieved a Dice score of 89.51% and an intersection-over-union (IoU) score of 86.54% on the UW-Madison Gastrointestinal Segmentation dataset. Moreover, our method attained a Dice score of 78.77% and a Hausdorff distance (HD) of 27.94% on the Synapse Multi-organ Segmentation dataset. Compared with the state-of-the-art methods, our proposed method achieves superior segmentation performance in gastrointestinal segmentation tasks. More significantly, our method can be easily extended to medical segmentation in different modalities such as CT and MRI. Our method achieves clinical multimodal medical segmentation and provides decision supports for clinical radiotherapy plans.

Mangrove monitoring revealed by MDPrePost-Net using archived Landsat imageries

Acting as a global vital entity in the coastal ecosystem, mangroves are currently facing major threats to destruction due to anthropogenic activities. Restoration and rehabilitation measures of mangroves are being carried out, including in Indonesia. To support conservation actions in many islands in Indonesia, spatio-temporal information on mangroves is required. Remote sensing with advanced techniques such as machine learning and deep learning has proven the capability to spatially observe mangroves. This study aims to monitor mangrove change in three study areas, i.e., South Sumatra, North Kalimantan, and Southeast Sulawesi, using a fully convolutional network (FCN)-based MDPrePost-Net. This method was developed originally to assess the mangrove degradation due to a major event (i.e., Hurricane irma 2017 in southwest Florida), whereas this study adopts it for an extended observation period (1989–2022 for South Sumatra, 1991–2021 for North Kalimantan, and 1990–2021 for Southeast Sulawesi) using medium-resolution Landsat imageries. We observed that the mangroves remain stable within the national parks designated by the government. Outside the national parks, mangrove conversion massively occurred even though the areas are assigned as protection forests. The classification results showed satisfactory accuracy values of more than 84%. The maps produced have an advantage in the spatial change analysis compared to the global datasets such as Global Mangrove Watch version 3.0. Our method has a limitation when cloudless images are not available. The integration with Synthetic Aperture Radar (SAR) images and a rigorous cloud removal method for the optical images may improve the results of mangrove monitoring.

Cattle Target Segmentation Method in Multi-Scenes Using Improved DeepLabV3+ Method.

Obtaining animal regions and the relative position relationship of animals in the scene is conducive to further studying animal habits, which is of great significance for smart animal farming. However, the complex breeding environment still makes detection difficult. To address the problems of poor target segmentation effects and the weak generalization ability of existing semantic segmentation models in complex scenes, a semantic segmentation model based on an improved DeepLabV3+ network (Imp-DeepLabV3+) was proposed. Firstly, the backbone network of the DeepLabV3+ model was replaced by MobileNetV2 to enhance the feature extraction capability of the model. Then, the layer-by-layer feature fusion method was adopted in the Decoder stage to integrate high-level semantic feature information with low-level high-resolution feature information at multi-scale to achieve more precise up-sampling operation. Finally, the SENet module was further introduced into the network to enhance information interaction after feature fusion and improve the segmentation precision of the model under complex datasets. The experimental results demonstrate that the Imp-DeepLabV3+ model achieved a high pixel accuracy (PA) of 99.4%, a mean pixel accuracy (MPA) of 98.1%, and a mean intersection over union (MIoU) of 96.8%. Compared to the original DeepLabV3+ model, the segmentation performance of the improved model significantly improved. Moreover, the overall segmentation performance of the Imp-DeepLabV3+ model surpassed that of other commonly used semantic segmentation models, such as Fully Convolutional Networks (FCNs), Lite Reduced Atrous Spatial Pyramid Pooling (LR-ASPP), and U-Net. Therefore, this study can be applied to the field of scene segmentation and is conducive to further analyzing individual information and promoting the development of intelligent animal farming.

FedHM: Practical federated learning for heterogeneous model deployments

In this paper, we propose a novel federated learning framework named FedHM that aims to address the challenge of training models on heterogeneous devices with varying architectures. Our approach enables the collaborative training of diverse local models by sharing a fully convolutional network (FCN) architecture that effectively extracts the local-to-global representations. By leveraging the weights with respect to this abstraction as common information across different DNN architectures, FedHM achieves efficient federated learning with minimal computational and communication overhead. We compare FedHM with three federated learning frameworks using two datasets for image classification tasks. Our results show that FedHM achieves high accuracy with considerably lower computational and communication costs compared to the other frameworks.

Welding Groove Edge Detection Method Using Lightweight Fusion Model Based on Transfer Learning

Groove edge detection is the prerequisite for weld seam deviation identification. A welding groove edge detection method based on transfer learning is presented as a solution to the inaccuracy of the conventional image processing method for extracting the edge of the welding groove. DenseNet and MobileNetV2 are used as feature extractors for transfer learning. Dense-Mobile Net is constructed using the skip connections structure and depthwise separable convolution. The Dense-Mobile Net training procedure consists of two stages: pre-training and model fusion fine-tuning. Experiments demonstrate that the proposed model accurately detects groove edges in MAG welding images. Using MIG welding images and the Pascal VOC2012 dataset to evaluate the generalization ability of the model, the relevant indicators are greater than those of Support Vector Machine (SVM), Fully Convolutional Networks (FCN), and UNet. The average single-frame detection time of the proposed model is 0.14 s, which meets the requirements of industrial real-time performance.

Tunnel Lining Crack Recognition Algorithm Integrating SK Attention and Cascade Neural Network

Cracks are one of the main types of tunnel-lining defects. At present, there are no particularly good methods for identifying tunnel-lining cracks. The methods that are used are associated with problems such as poor robustness, low detection efficiency, and inconsistency in defect identification. Vision-based crack identification algorithms that use images of tunnel-lining cracks are also affected by problems such as weak features, irregular shapes, random development, and small proportions. In light of this, we propose in this paper a neural network that integrates U-net and FPN (feature pyramid network) pyramid structure characteristics. Because of its ability to output deep feature information, an SK (selective kernel) attention mechanism was integrated to increase the weight of effective feature regions and improve the detection accuracy of feature maps. An RPN (region proposal network) regional detection network was used for multi-scale regional screening, and non-maximum suppression was used to eliminate repeated anchor frames between different regions. The continuity of cracks was determined using adaptive regional expansion. The region segmentation network was also combined with multi-scale feature maps. Finally, the segmentation results for regions of fine features could be mapped to the whole image to identify cracks, thereby solving the problem that arises when textural features of cracks are weak and cannot be accurately identified. For testing purposes, photos of tunnel-lining crack defects were used. The algorithm described in this paper was able to combine deep and shallow features to identify more abundant crack features, and the recognition accuracy of the crack classification network reached 98.51%. With respect to crack segmentation, the algorithm’s segmentation accuracy reached 94.55%, and its single processing time reached 60.76 ms, indicating more accurate and efficient segmentation performance, compared with FCNs (fully convolutional networks) and classical U-net networks.

Occlusion-Aware Instance Segmentation Via BiLayer Network Architectures.

Segmenting highly-overlapping image objects is challenging, because there is typically no distinction between real object contours and occlusion boundaries on images. Unlike previous instance segmentation methods, we model image formation as a composition of two overlapping layers, and propose Bilayer Convolutional Network (BCNet), where the top layer detects occluding objects (occluders) and the bottom layer infers partially occluded instances (occludees). The explicit modeling of occlusion relationship with bilayer structure naturally decouples the boundaries of both the occluding and occluded instances, and considers the interaction between them during mask regression. We investigate the efficacy of bilayer structure using two popular convolutional network designs, namely, Fully Convolutional Network (FCN) and Graph Convolutional Network (GCN). Further, we formulate bilayer decoupling using the vision transformer (ViT), by representing instances in the image as separate learnable occluder and occludee queries. Large and consistent improvements using one/two-stage and query-based object detectors with various backbones and network layer choices validate the generalization ability of bilayer decoupling, as shown by extensive experiments on image instance segmentation benchmarks (COCO, KINS, COCOA) and video instance segmentation benchmarks (YTVIS, OVIS, BDD100 K MOTS), especially for heavy occlusion cases.

Attention U-Net Based on Bi-ConvLSTM and Its Optimization for Smart Healthcare

As an important part of cyber–physical–social intelligence, artificial intelligence (AI)-driven smart healthcare is committed to promoting the application of human–machine hybrid augmented intelligence in the medical field, including AI-assisted medical image analysis and lesion recognition. Among them, deep learning models represented by fully convolutional networks (FCNs) have achieved excellent performance in medical image segmentation. However, limited by the complex structure of segmentation networks and the inherently redundant characteristics of convolutional operation, the scale of these models is extremely large. To further promote the application of machine intelligence in the field of medical image analysis, we propose an attention U-Net based on Bi-ConvLSTM (AUBC-Net) for accurate segmentation of medical images in this article. Different from classical U-Net, the proposed model deals with the potential association between decoding features and encoding features by bidirectional convolution LSTM. Furthermore, for the inherent redundancy characteristics of FCNs, we propose a lightweight feature generation strategy and optimize the calculation process of Bi-ConvLSTM based on tensor multilinear algebra, which can greatly reduce the number of network parameters. In addition, we have conducted the image segmentation experiments on two benchmark medical datasets, and the experimental results demonstrate that the proposed model can not only achieve better performance than existing methods, but also effectively compress network parameters while ensuring performance, which greatly facilitates AI-driven smart medical applications.

Open-Pit Mining Area Extraction from High-Resolution Remote Sensing Images Based on EMANet and FC-CRF

Rapid and accurate identification of open-pit mining areas is essential for guiding production planning and assessing environmental impact. Remote sensing technology provides an effective means for open-pit mine boundary identification. In this study, an effective method for delineating an open-pit mining area from remote sensing images is proposed, which is based on the deep learning model of the Expectation-Maximizing Attention Network (EMANet) and the fully connected conditional random field (FC-CRF) algorithm. First, ResNet-34 was applied as the backbone network to obtain preliminary features. Second, the EMA mechanism was used to enhance the learning of important information and details in the image. Finally, a postprocessing program based on FC-CRF was introduced to optimize the initial prediction results. Meanwhile, the extraction effect of MobileNetV3, U-Net, fully convolutional network (FCN), and our method were compared on the same data set for the open-pit mining areas. The advantage of the model is verified by the visual graph results, and the accuracy evaluation index based on the confusion matrix calculation. pixel accuracy (PA), mean intersection over union (MIoU), and kappa were 98.09%, 89.48%, and 88.48%, respectively. The evaluation results show that this method effectively identifies open-pit mining areas. It is of practical significance to complete the extraction task of open-pit mining areas accurately and comprehensively, which can be used for production management and environmental protection of open-pit mines.

AFLFP: A Database With Annotated Facial Landmarks for Facial Palsy

Facial landmark detection is a crucial step for the task of computer-aided facial palsy diagnosis, which enables to focus on the affected facial regions for learning asymmetry, shape, and texture features of facial palsy. However, it is still very challenging to accurately detect salient landmarks on facial palsy images due to the unavailability of sufficient training databases providing annotated facial landmark images of facial palsy. To this end, we present a database in this article named annotated facial landmarks for facial palsy (AFLFP). AFLFP is a diverse, and reliable database that contains facial images with 16-class facial expressions of asymmetric facial expressions from 88 subjects. Each facial image is independently and manually annotated with 68 facial landmarks. This database is the first public manually annotated facial landmark database for facial palsy so far. Furthermore, to establish the benchmark results for the proposed database, we propose a deep neural network (DNN) baseline with a two-stage cascaded fully convolutional network (FCN), which can detect facial landmarks in facial palsy from coarse to fine. The comprehensive experiments show that the proposed method performs better than the mainstream methods of machine-and deep-learning. And we have also compared the performance using normal-and palsy-faces, respectively, as the training data. The comparison results show that there are significant differences between them in terms of facial landmark detection, which further confirms the necessity to develop a facial landmark database specifically for facial palsy.

Deep learning based automatic Debakey classification of aortic dissection using CT plain scan

ObjectiveThis paper presents an automatic segmentation and classification of aortic dissection by utilizing neural network based on CT scanning. MethodologyFirst we present a way of establishing models of indivisible aorta parametrization, designed multiple seed points as the initial parameter and based on the three-dimensional area growth algorithm, finally add morphological processing to realize the segmentation of inner membrane, and then introduced the semantic segmentation framework, and analyzed the characteristics and function of each part, improve the Deeplabv3 + network. This paper analyzes the results of the different experiments with two-dimensional lattices and presents the advantages of being sensitive to differences in shape and position and of not being able to use three-dimensional spatial continuity. Our research establishs the aorta reconstruction, which is simpler for learning the transformation of both the shape and position of, according to a last for three-dimensional CT images interfloor of the entire helix network aorta 3D MPRNet.A new module of multi-scale receptive field information fusion is designed, which realizes the function of acquiring different receptive field features, taking into account details and global features, so that the model obtains the unique ability more suitable for segmentation problems. In the decoder stage, the cross-layer connection mode similar to U-Net is still adopted to realize the fusion of semantic information at high and low levels, which ensures that the network can learn enough features. ResultsThe full convolutional network and traditional algorithms based on domain knowledge of aortic dissection images achieve a robust, automated and high precision CT image aortic dissection segmentation scheme. ConclusionThe large number of decoder parameters is increased, which increases the difficulty of network training and reduces the reasoning speed of the model. At the same time, not all features are useful information, while the high frequency and low frequency, the feature graph only adopts cross-layer connection to connect shallow features to high-level information may bring the redundancy of information and interference with useful information. Therefore, designing an efficient decoder is another research idea to accelerate model inference and improve segmentation accuracy.