Cross-layer Connection Research Articles

Lightweight Detection of Broccoli Heads in Complex Field Environments Based on LBDC-YOLO

Robotically selective broccoli harvesting requires precise lightweight detection models to efficiently detect broccoli heads. Therefore, this study introduces a lightweight and high-precision detection model named LBDC-YOLO (Lightweight Broccoli Detection in Complex Environment—You Look Only Once), based on the improved YOLOv8 (You Look Only Once, Version 8). The model incorporates the Slim-neck design paradigm based on GSConv to reduce computational complexity. Furthermore, Triplet Attention is integrated into the backbone network to capture cross-dimensional interactions between spatial and channel dimensions, enhancing the model’s feature extraction capability under multiple interfering factors. The original neck network structure is replaced with a BiFPN (Bidirectional Feature Pyramid Network), optimizing the cross-layer connection structure, and employing weighted fusion methods for better integration of multi-scale features. The model undergoes training and testing on a dataset constructed in real field conditions, featuring broccoli images under various influencing factors. Experimental results demonstrate that LBDC-YOLO achieves an average detection accuracy of 94.44% for broccoli. Compared to the original YOLOv8n, LBDC-YOLO achieves a 32.1% reduction in computational complexity, a 47.8% decrease in parameters, a 44.4% reduction in model size, and a 0.47 percentage point accuracy improvement. When compared to models such as YOLOv5n, YOLOv5s, and YOLOv7-tiny, LBDC-YOLO exhibits higher detection accuracy and lower computational complexity, presenting clear advantages for broccoli detection tasks in complex field environments. The results of this study provide an accurate and lightweight method for the detection of broccoli heads in complex field environments. This work aims to inspire further research in precision agriculture and to advance knowledge in model-assisted agricultural practices.

Improved Architecture and Training Strategies of YOLOv7 for Remote Sensing Image Object Detection

The technology for object detection in remote sensing images finds extensive applications in production and people’s lives, and improving the accuracy of image detection is a pressing need. With that goal, this paper proposes a range of improvements, rooted in the widely used YOLOv7 algorithm, after analyzing the requirements and difficulties in the detection of remote sensing images. Specifically, we strategically remove some standard convolution and pooling modules from the bottom of the network, adopting stride-free convolution to minimize the loss of information for small objects in the transmission. Simultaneously, we introduce a new, more efficient attention mechanism module for feature extraction, significantly enhancing the network’s semantic extraction capabilities. Furthermore, by adding multiple cross-layer connections in the network, we more effectively utilize the feature information of each layer in the backbone network, thereby enhancing the network’s overall feature extraction capability. During the training phase, we introduce an auxiliary network to intensify the training of the underlying network and adopt a new activation function and a more efficient loss function to ensure more effective gradient feedback, thereby elevating the network performance. In the experimental results, our improved network achieves impressive mAP scores of 91.2% and 80.8% on the DIOR and DOTA version 1.0 remote sensing datasets, respectively. These represent notable improvements of 4.5% and 7.0% over the original YOLOv7 network, significantly enhancing the efficiency of detecting small objects in particular.

A Lightweight Cross-Layer Smoke-Aware Network.

Smoke is an obvious sign of pre-fire. However, due to its variable morphology, the existing schemes are difficult to extract precise smoke characteristics, which seriously affects the practical applications. Therefore, we propose a lightweight cross-layer smoke-aware network (CLSANet) of only 2.38 M. To enhance the information exchange and ensure accurate feature extraction, three cross-layer connection strategies with bias are applied to the CLSANet. First, a spatial perception module (SPM) is designed to transfer spatial information from the shallow layer to the high layer, so that the valuable texture details can be complemented in the deeper levels. Furthermore, we propose a texture federation module (TFM) in the final encoding phase based on fully connected attention (FCA) and spatial texture attention (STA). Both FCA and STA structures implement cross-layer connections to further repair the missing spatial information of smoke. Finally, a feature self-collaboration head (FSCHead) is devised. The localization and classification tasks are decoupled and explicitly deployed on different layers. As a result, CLSANet effectively removes redundancy and preserves meaningful smoke features in a concise way. It obtains the precision of 94.4% and 73.3% on USTC-RF and XJTU-RS databases, respectively. Extensive experiments are conducted and the results demonstrate that CLSANet has a competitive performance.

S5Utis: Structured State-Space Sequence SegNeXt UNet-like Tongue Image Segmentation in Traditional Chinese Medicine.

Intelligent Traditional Chinese Medicine can provide people with a convenient way to participate in daily health care. The ease of acceptance of Traditional Chinese Medicine is also a major advantage in promoting health management. In Traditional Chinese Medicine, tongue imaging is an important step in the examination process. The segmentation and processing of the tongue image directly affects the results of intelligent Traditional Chinese Medicine diagnosis. As intelligent Traditional Chinese Medicine continues to develop, remote diagnosis and patient participation will play important roles. Smartphone sensor cameras can provide irreplaceable data collection capabilities in enhancing interaction in smart Traditional Chinese Medicine. However, these factors lead to differences in the size and quality of the captured images due to factors such as differences in shooting equipment, professionalism of the photographer, and the subject's cooperation. Most current tongue image segmentation algorithms are based on data collected by professional tongue diagnosis instruments in standard environments, and are not able to demonstrate the tongue image segmentation effect in complex environments. Therefore, we propose a segmentation algorithm for tongue images collected in complex multi-device and multi-user environments. We use convolutional attention and extend state space models to the 2D environment in the encoder. Then, cross-layer connection fusion is used in the decoder part to fuse shallow texture and deep semantic features. Through segmentation experiments on tongue image datasets collected by patients and doctors in real-world settings, our algorithm significantly improves segmentation performance and accuracy.

A Fast Feedforward Small-World Neural Network for Nonlinear System Modeling.

It is well-documented that cross-layer connections in feedforward small-world neural networks (FSWNNs) enhance the efficient transmission for gradients, thus improving its generalization ability with a fast learning. However, the merits of long-distance cross-layer connections are not fully utilized due to the random rewiring. In this study, aiming to further improve the learning efficiency, a fast FSWNN (FFSWNN) is proposed by taking into account the positive effects of long-distance cross-layer connections, and applied to nonlinear system modeling. First, a novel rewiring rule by giving priority to long-distance cross-layer connections is proposed to increase the gradient transmission efficiency when constructing FFSWNN. Second, an improved ridge regression method is put forward to determine the initial weights with high activation for the sigmoidal neurons in FFSWNN. Finally, to further improve the learning efficiency, an asynchronous learning algorithm is designed to train FFSWNN, with the weights connected to the output layer updated by the ridge regression method and other weights by the gradient descent method. Several experiments are conducted on four benchmark datasets from the University of California Irvine (UCI) machine learning repository and two datasets from real-life problems to evaluate the performance of FFSWNN on nonlinear system modeling. The results show that FFSWNN has significantly faster convergence speed and higher modeling accuracy than the comparative models, and the positive effects of the novel rewiring rule, the improved weight initialization, and the asynchronous learning algorithm on learning efficiency are demonstrated.

Multi-attention fusion transformer for single-image super-resolution

Recently, Transformer-based methods have gained prominence in image super-resolution (SR) tasks, addressing the challenge of long-range dependence through the incorporation of cross-layer connectivity and local attention mechanisms. However, the analysis of these networks using local attribution maps has revealed significant limitations in leveraging the spatial extent of input information. To unlock the inherent potential of Transformer in image SR, we propose the Multi-Attention Fusion Transformer (MAFT), a novel model designed to integrate multiple attention mechanisms with the objective of expanding the number and range of pixels activated during image reconstruction. This integration enhances the effective utilization of input information space. At the core of our model lies the Multi-attention Adaptive Integration Groups, which facilitate the transition from dense local attention to sparse global attention through the introduction of Local Attention Aggregation and Global Attention Aggregation blocks with alternating connections, effectively broadening the network's receptive field. The effectiveness of our proposed algorithm has been validated through comprehensive quantitative and qualitative evaluation experiments conducted on benchmark datasets. Compared to state-of-the-art methods (e.g. HAT), the proposed MAFT achieves 0.09 dB gains on Urban100 dataset for × 4 SR task while containing 32.55% and 38.01% fewer parameters and FLOPs, respectively.

Improved YOLOv8 for B-scan image flaw detection of the heavy-haul railway

With the high speed and heavy duty of railway transportation, internal flaw detection of railway rails has become a hot issue. Existing rail flaw detection systems have problems of low detection accuracy and occasional missed flaw detection. In this paper, a high-precision flaw detection based on data augmentation and YOLOv8 improvement is proposed. Firstly, three data augmentation algorithms based on the characteristics of B-scan images are designed to enrich the dataset of rail flaws. Then, the small target detection layer and the cross-layer connectivity module are added to capture more information for small targets. Finally, the introduction of dynamic weights to coordinate attention can adjust the attentional weights and capture long-range information. The experimental results show that the mAP50 of the model after data enhancement and algorithm improvement is 97.9%, which is improved by 4.4% from the baseline model, and the frame per second is 64.52. The proposed method effectively detects many typical flaws, including the railhead flaw, rail jaw flaw, screw hole crack, and bottom flaw, which can provide technology supports for on-site maintenance staff.

A safety helmet-wearing detection method based on cross-layer connection

A safety helmet-wearing detection method based on cross-layer connection

An Intelligent Detection and Classification Model Based on Computer Vision for Pavement Cracks in Complicated Scenarios

With the extension of road service life, cracks are the most significant type of pavement distress. To monitor road conditions and avoid excessive damage, pavement crack detection is absolutely necessary and an indispensable part of road periodic maintenance and performance assessment. The development and application of computer vision have provided modern methods for crack detection, which are low in cost, less labor-intensive, continuous, and timely. In this paper, an intelligent model based on a target detection algorithm in computer vision was proposed to accurately detect and classify four classes of cracks. Firstly, by vehicle-mounted camera capture, a dataset of pavement cracks with complicated backgrounds that are the most similar to actual scenarios was built, containing 4007 images and 7882 crack samples. Secondly, the YOLOv5 framework was improved from the four aspects of the detection layer, anchor box, neck structure, and cross-layer connection, and thereby the network’s feature extraction capability and small-sized-target detection performance were enhanced. Finally, the experimental results indicated that the proposed model attained an AP of the four classes of 81.75%, 83.81%, 98.20%, and 92.83%, respectively, and a mAP of 89.15%. In addition, the proposed model achieved a 2.20% missed detection rate, representing a 6.75% decrease over the original YOLOv5. These results demonstrated the effectiveness and practicality of our proposed model in addressing the issues of low accuracy and missed detection for small targets in the original network. Overall, the implementation of computer vision-based models in crack detection can promote the intellectualization of road maintenance.

Study on breast cancer image detection and classification based on residual connected convolutional neural network (CNN)

This paper presents a method for breast cancer image detection and classification based on convolutional neural networks. While traditional breast cancer detection methods carry the risk of subjectivity and misclassification, machine learning can improve the accuracy and efficiency of detection by training algorithms to automatically identify breast cancer lesions in patients. The dataset of this paper includes 7909 microscopic images of breast tumor tissues with different magnifications collected from 82 patients, which are divided into two categories: benign and malignant tumors. In this paper, residual connection is designed for convolutional neural networks, which is a cross-layer connection method that can effectively solve the gradient vanishing and gradient explosion problems in deep neural networks, and at the same time, improve the generalization ability and training speed of the model. In this paper, the dataset is divided into training set, validation set and test set according to the ratio of 6:2:2. Through training and validation, the prediction accuracy of the obtained model on the test set reaches 85.5%, which achieves good prediction results in breast cancer image detection and classification. Eventually, the model's loss converged at 0.53 and the AUC was 0.832. The research results in this paper are of great significance for the early diagnosis and treatment of breast cancer. Automatic identification and classification of breast cancer images by machine learning algorithms can reduce the subjective judgment of doctors, improve the accuracy and efficiency of detection, and provide important support for the early diagnosis and treatment of breast cancer. Meanwhile, the research method and results of this paper also provide reference and reference for image detection and classification of other malignant tumors.

A precise model for skin cancer diagnosis using hybrid U-Net and improved MobileNet-V3 with hyperparameters optimization

Skin cancer is a frequently occurring and possibly deadly disease that necessitates prompt and precise diagnosis in order to ensure efficacious treatment. This paper introduces an innovative approach for accurately identifying skin cancer by utilizing Convolution Neural Network architecture and optimizing hyperparameters. The proposed approach aims to increase the precision and efficacy of skin cancer recognition and consequently enhance patients' experiences. This investigation aims to tackle various significant challenges in skin cancer recognition, encompassing feature extraction, model architecture design, and optimizing hyperparameters. The proposed model utilizes advanced deep-learning methodologies to extract complex features and patterns from skin cancer images. We enhance the learning procedure of deep learning by integrating Standard U-Net and Improved MobileNet-V3 with optimization techniques, allowing the model to differentiate malignant and benign skin cancers. Also substituted the crossed-entropy loss function of the Mobilenet-v3 mathematical framework with a bias loss function to enhance the accuracy. The model's squeeze and excitation component was replaced with the practical channel attention component to achieve parameter reduction. Integrating cross-layer connections among Mobile modules has been proposed to leverage synthetic features effectively. The dilated convolutions were incorporated into the model to enhance the receptive field. The optimization of hyperparameters is of utmost importance in improving the efficiency of deep learning models. To fine-tune the model's hyperparameter, we employ sophisticated optimization methods such as the Bayesian optimization method using pre-trained CNN architecture MobileNet-V3. The proposed model is compared with existing models, i.e., MobileNet, VGG-16, MobileNet-V2, Resnet-152v2 and VGG-19 on the “HAM-10000 Melanoma Skin Cancer dataset". The empirical findings illustrate that the proposed optimized hybrid MobileNet-V3 model outperforms existing skin cancer detection and segmentation techniques based on high precision of 97.84%, sensitivity of 96.35%, accuracy of 98.86% and specificity of 97.32%. The enhanced performance of this research resulted in timelier and more precise diagnoses, potentially contributing to life-saving outcomes and mitigating healthcare expenditures.

UCM-Net: A U-Net-Like Tampered-Region-Related Framework for Copy-Move Forgery Detection

Copy-move forgery causes a big challenge to copy-move forgery detection (CMFD) due to that the photometrical characteristics of genuine and tampered regions in the same image remain highly consistent. A novel U-Net-like architecture with multiple asymmetric cross-layer connections associated with self-correlation and atrous spatial pyramid pooling (ASPP) between feature extraction module (FEM) and tampered region localization module (TRLM), called UCM-Net, is proposed in this paper. Different from existing deep learning based CMFD networks which indiscriminately process large or small tampered regions without considering the statistical characteristics of regions, FEM differentially treats large or small tampered regions by exploiting deep backbone networks to extract high-level features with rich semantic information for large tampered regions while utilizing lightweight backbone networks to extract low-level features for small tampered regions. Multiple cross-layer connections between two modules utilize the self-correlation calculation and ASPP to remove as much irrelevant semantic information as possible while retaining multi-scale tampered features from shallow to deep convolutional layers of FEM. Unlike the previous CMFD networks, which cannot capture multi-scale features because of simply stacking convolution blocks in the upsampling step, TRLM exploits multiple U-shaped residual U-block modules with different depths to change the receptive field of each point in the tampered feature maps so as to capture global and local information, greatly improving the localization accuracy of tampered regions. Experimental results on three publicly available databases demonstrate that UCM-Net outperforms several state-of-the-art algorithms in terms of various evaluation metrics.

TransCNNLoc: End-to-end pixel-level learning for 2D-to-3D pose estimation in dynamic indoor scenes

Accurate localization in GPS-denied environments has always been a core issue in computer vision and robotics research. In indoor environments, vision-based localization methods are susceptible to changes in lighting conditions, viewing angles, and environmental factors, resulting in localization failures or limited generalization capabilities. In this paper, we propose the TransCNNLoc framework, which consists of an encoding–decoding network designed to learn more robust image features for camera pose estimation. In the image feature encoding stage, CNN and Swin Transformer are integrated to construct the image feature encoding module, enabling the network to fully extract global context and local features from images. In the decoding stage, multi-level image features are decoded through cross-layer connections while computing per-pixel feature weight maps. To enhance the framework’s robustness to dynamic objects, a dynamic object recognition network is introduced to optimize the feature weights. Finally, a multi-level iterative optimization from coarse to fine levels is performed to recover six degrees of freedom camera pose. Experiments were conducted on the publicly available 7scenes dataset as well as a dataset collected under changing lighting conditions and dynamic scenes for accuracy validation and analysis. The experimental results demonstrate that the proposed TransCNNLoc framework exhibits superior adaptability to dynamic scenes and lighting changes. In the context of static environments within publicly available datasets, the localization technique introduced in this study attains a maximal precision of up to 5 centimeters, consistently achieving superior outcomes across a majority of the scenarios. Under the conditions of dynamic scenes and fluctuating illumination, this approach demonstrates an enhanced precision capability, reaching up to 3 centimeters. This represents a substantial refinement from the decimeter scale to a centimeter scale in precision, marking a significant advancement over the existing state-of-the-art (SOTA) algorithms. The open-source repository for the method proposed in this paper can be found at the following URL: github.com/Geelooo/TransCNNloc.

Multi-scale cross-layer fusion and center position network for pedestrian detection

Pedestrian detection has made breakthroughs after the rise of convolutional neural networks. However, it faces some challenging problems, including dataset difference, small pedestrian targets and occlusions between pedestrians. To deal with these problems, we propose a novel convolutional network architecture, named multi-scale cross-layer fusion and center position network (MCF-CP-NET). A new backbone unit is designed to introduce channel-wise attention into the improved aggregated residual transformations for effective feature extraction. We select suitable anchor setting for pedestrian detection datasets to tackle the problem of dataset difference. A feature pyramid sub-network with cross-layer fusion is developed for better detection of small pedestrian targets, where cross-layer connections are used to reduce the information loss and low-level marginal feature dissipation and better fuse low- and high-level features. We add a center position branch into the localization regression sub-network in MCF-CP-NET to better detect occluded pedestrians, which predicts the centrality index of the localization box to obtain the center score, and further optimizes the score of non-maximum suppression. Experiments show that the average precision and recall of MCF-CP-NET are separately improved by 1.2% and 0.7% on the person class of Pascal VOC2007 dataset and 1.6% and 0.1% on WiderPerson dataset, in comparison with the state-of-the-art.

Cross-layer connected network with adaptive attention mechanism for 3D multi-organ and tumor segmentations from CT

3D medical image segmentation is one of the most important and challenging tasks for medical image analysis. To enhance the feature extraction ability of 3D convolutional neural networks (CNN) and improve the segmentation accuracy on small tumors, this paper presents a cross-layer connected network with an adaptive attention mechanism to achieve efficient segmentations for 3D multi-organ and tumor from CT. A cross layer connection is proposed to utilize features more effectively by reusing multiple layers of semantic information while recovering spatial information. The pre-activation Residual Block and Squeeze-and-Excitation (SE) block are introduced to enhance the feature extraction ability. Due to the serious class imbalance problem caused by small tumors, the training process of the 3D CNN model is unstable and small targets are difficult to segment. To relieve the training instability problem, Anti-Aliased Pooling (AAP) is introduced to replace the original maxpooling. Moreover, a region of interest (ROI) adaptive attention mechanism based on the auxiliary loss function is developed to improve the segmentation accuracy on small target tumors by combining the target detection method with semantic segmentation models. Extensive experiments show that the dice coefficient of 3D liver tumor segmentation on the LiTS dataset reaches 0.668 by using our designed network, and the experiments on the BRATS dataset for 3D brain tumor segmentation yield average dice coefficient, sensitivity, and Hausdorff distance of 0.706, 0.730, and 5.972 mm, respectively, which are obviously superior to some existing methods.

A Multi-Scale Recursive Attention Feature Fusion Network for Image Super-Resolution Reconstruction Algorithm

In recent years, deep convolutional neural networks (CNNs) have made significant progress in single-image super-resolution (SISR) tasks. Despite their good performance, the single-image super-resolution task remains a challenging one due to problems with underutilization of feature information and loss of feature details. In this paper, a multi-scale recursive attention feature fusion network (MSRAFFN) is proposed for this purpose. The network consists of three parts: a shallow feature extraction module, a multi-scale recursive attention feature fusion module, and a reconstruction module. The shallow features of the image are first extracted by the shallow feature extraction module. Then, the feature information at different scales is extracted by the multi-scale recursive attention feature fusion network block (MSRAFFB) to enhance the channel features of the network through the attention mechanism and fully fuse the feature information at different scales in order to improve the network’s performance. In addition, the image features at different levels are integrated through cross-layer connections using residual connections. Finally, in the reconstruction module, the upsampling capability of the deconvolution module is used to enlarge the image while extracting its high-frequency information in order to obtain a sharper high-resolution image and achieve a better visual effect. Through extensive experiments on a benchmark dataset, the proposed network model is shown to have better performance than other models in terms of both subjective visual effects and objective evaluation metrics.

An Exploration of the Reform of English Informatisation Teaching in Colleges and Universities Based on Deep Learning Model and Microteaching Mode

Abstract In this paper, we use the cross-layer connectivity of residual networks in deep learning to convert convolutional and fully connected layers into sparse connections and cluster sparse matrices into relatively dense subspaces. Extracted features are used to perform target class prediction and regression of target coordinates using a target detection algorithm to meet the demand for real-time target detection. The model's use resulted in a head-up rate of 83.57% in the classroom, with the least serious students at 0.8 and above. Deep learning technology can enhance students' learning experience in English classrooms by providing personalized learning and a deep learning environment.

Decoding Algorithm of Motor Imagery Electroencephalogram Signal Based on CLRNet Network Model.

EEG decoding based on motor imagery is an important part of brain-computer interface technology and is an important indicator that determines the overall performance of the brain-computer interface. Due to the complexity of motor imagery EEG feature analysis, traditional classification models rely heavily on the signal preprocessing and feature design stages. End-to-end neural networks in deep learning have been applied to the classification task processing of motor imagery EEG and have shown good results. This study uses a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) network to obtain spatial information and temporal correlation from EEG signals. The use of cross-layer connectivity reduces the network gradient dispersion problem and enhances the overall network model stability. The effectiveness of this network model is demonstrated on the BCI Competition IV dataset 2a by integrating CNN, BiLSTM and ResNet (called CLRNet in this study) to decode motor imagery EEG. The network model combining CNN and BiLSTM achieved 87.0% accuracy in classifying motor imagery patterns in four classes. The network stability is enhanced by adding ResNet for cross-layer connectivity, which further improved the accuracy by 2.0% to achieve 89.0% classification accuracy. The experimental results show that CLRNet has good performance in decoding the motor imagery EEG dataset. This study provides a better solution for motor imagery EEG decoding in brain-computer interface technology research.

Optical Remote Sensing Ship Recognition and Classification Based on Improved YOLOv5

Due to the special characteristics of the shooting distance and angle of remote sensing satellites, the pixel area of ship targets is small, and the feature expression is insufficient, which leads to unsatisfactory ship detection performance and even situations such as missed and false detection. To solve these problems, this paper proposes an improved-YOLOv5 algorithm mainly including: (1) Add the Convolutional Block Attention Module (CBAM) into the Backbone to enhance the extraction of target-adaptive optimal features; (2) Introduce a cross-layer connection channel and lightweight GSConv structures into the Neck to achieve higher-level multi-scale feature fusion and reduce the number of model parameters; (3) Use the Wise-IoU loss function to calculate the localization loss in the Output, and assign reasonable gradient gains to cope with differences in image quality. In addition, during the preprocessing stage of experimental data, a median+bilateral filter method was used to reduce interference from ripples and waves and highlight the information of ship features. The experimental results show that Improved-YOLOv5 has a significant improvement in recognition accuracy compared to various mainstream target detection algorithms; compared to the original YOLOv5s, the mean Average Precision (mAP) improved by 3.2% and the Frames Per Second (FPN) accelerated by 8.7%.

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.