Network Traffic Classification Model Based on Multi-scale Feature Fusion and Attention Mechanism (MSF-AttNet)

Underwater robots are an important tool for exploiting marine resources, while underwater target detection is a key technology for marine fishing by underwater robots. For the problem that the traditional algorithm is difficult to detect small targets and multiple scales in the underwater environment, this paper proposes an underwater biological detection algorithm based on the multi-scale feature fusion and attention mechanism. First, a YOLOv5 network is constructed, and a bi-directionally weighted BiFPN feature pyramid structure is fed into the neck network so that multiscale target features can be efficiently merged. Second, a channel and spatial convolutional attention mechanism is introduced between the neck network and the prediction network in order to increase the saliency of the underwater target that needs to be detected, which effectively reduces the missed and erroneous detection of underwater targets. The experimental results show that the proposed algorithm can accurately detect multi-scale underwater targets in complex underwater environments with an average accuracy of 80.18%, which is 2.87% better than YOLOv5, and an average recall of 85.06%, which is 4.81% better than YOLOv5.

Image dehazing is critical for visual enhancement and a wide range of computer vision applications. Despite significant advancements, challenges remain in preserving fine details and adapting to diverse, non-uniformly degraded scenes. To address these issues, we propose a novel image dehazing method that introduces a contrastive learning framework, enhanced by the InfoNCE loss, to improve model robustness. In this framework, hazy images are treated as negative samples and their clear counterparts as positive samples. By optimizing the InfoNCE loss, the model is trained to maximize the similarity between positive pairs and minimize that between negative pairs, thereby improving its ability to distinguish haze artifacts from intrinsic scene features and better preserving the structural integrity of images. In addition to contrastive learning, our method integrates a multi-scale dynamic feature fusion with a hybrid attention mechanism. Specifically, we introduce dynamically adjustable frequency band filters and refine the hybrid attention module to more effectively capture fine-grained, cross-scale image details. Extensive experiments on the RESIDE-6K and RS-Haze datasets demonstrate that our approach outperforms most existing methods, offering a promising solution for practical image dehazing applications.

Privacy information theft traffic is usually detected using traffic classification methods, and deep learning-based detection methods are effective for this task. However, these methods have complex preprocessing processes as well as tend to ignore the deep features of network traffic, while the generalization ability is not outstanding. In this paper, a network traffic classification and detection model TCCN (Traffic Classification Capsule Network) based on capsule network is proposed. Meanwhile, PacketCGAN-based data balancing method is introduced to assist TCCN in traffic classification. A new feature graph vectorization method is used to improve the efficiency of TCCN. In addition, TCCN uses dynamic routing mechanism that can retain more valid traffic characteristics. In parallel, this paper proposes a new loss function to improve the generalization performance of TCCN. The results show that TCCN can show good performance in different experimental scenarios. After effective training, TCCN can also show high detection accuracy in new datasets, and the generalization ability of the model reaches a relatively excellent level.

Beat Tracking Algorithm Based on Multi-scale Feature Fusion and Attention Mechanism

In view of the small size and dense distribution of remote sensing image targets, this paper adds a detection head P2 specifically for small-scale targets on the basis of the three detection layers of the original YOLOv5 model, and involves the shallow high-resolution feature map in the subsequent multiscale feature fusion module. The problem of losing the key feature information of the small-scale target in the process of multiple downsampling is effectively avoided. Firstly, an enhanced multi-scale feature fusion pyramid network DSI-FPN is designed. The FPN+PAN network is optimized by using DepthwiseSparable Convolution and Involution operators with fewer parameters and computations, as well as a spatial attention mechanism to generate feature graphs with richer information for network detection tasks. Secondly, we propose an adaptive channel spatial attention mechanism SCBAM, which introduces a self-attention mechanism into CBAM module to add non-local information to the interaction that originally had only local information, breaks the convolution kernel limit, expands the model receptive field, and improves the feature expression ability of the model. Thirdly, in order to solve the problem of insufficient computing power when deploying the target detector for equipment, we propose a network knowledge distillation framework for joint teachers based on the feature layer. The distillation loss of teacher is designed, and the trend of student online learning is adjusted dynamically by balancing the contributions of teacher network and truth value. The detection accuracy of the student network is obviously improved, and the parameters and model size of the network are effectively reduced. Finally, Comparing with other remote sensing image object detection methods, the experimental results show that the approach presented has better detection effect for small-scale targets of remote sensing images under different lighting conditions. The detection accuracy reached 43.9%, and 7.4% higher than that of the original model. After knowledge distillation, the model parameters are reduced to 1/3 of the original, and the detection accuracy is 40.2%. INDEX TERMS Deep Learning, object detection, remote sensing image, multi-scale feature fusion pyramid network, adaptive channel spatial attention mechanism, joint teacher knowledge distillation.

At present, it is challenging to extract landslides from high-resolution remote-sensing images using deep learning. Because landslides are very complex, the accuracy of traditional extraction methods is low. To improve the efficiency and accuracy of landslide extraction, a new model is proposed based on the U-Net model to automatically extract landslides from remote-sensing images: L-Unet. The main innovations are as follows: (1) A multi-scale feature-fusion (MFF) module is added at the end of the U-Net encoding network to improve the model’s ability to extract multi-scale landslide information. (2) A residual attention network is added to the U-Net model to deepen the network and improve the model’s ability to represent landslide features. (3) The bilinear interpolation algorithm in the decoding network of the U-Net model is replaced by data-dependent upsampling (DUpsampling) to improve the quality of the feature maps. Experimental results showed that the precision, recall, MIoU and F1 values of the L-Unet model are 4.15%, 2.65%, 4.82% and 3.37% higher than that of the baseline U-Net model, respectively. It was proven that the new model can extract landslides accurately and effectively.

Recently, with the advent of various Internet of Things (IoT) applications, a massive amount of network traffic is being generated. A network operator must provide different quality of service, according to the service provided by each application. Toward this end, many studies have investigated how to classify various types of application network traffic accurately. Especially, since many applications use temporary or dynamic IP or Port numbers in the IoT environment, only payload-based network traffic classification technology is more suitable than the classification using the packet header information as well as payload. Furthermore, to automatically respond to various applications, it is necessary to classify traffic using deep learning without the network operator intervention. In this study, we propose a traffic classification scheme using a deep learning model in software defined networks. We generate flow-based payload datasets through our own network traffic pre-processing, and train two deep learning models: 1) the multi-layer long short-term memory (LSTM) model and 2) the combination of convolutional neural network and single-layer LSTM models, to perform network traffic classification. We also execute a model tuning procedure to find the optimal hyper-parameters of the two deep learning models. Lastly, we analyze the network traffic classification performance on the basis of the F1-score for the two deep learning models, and show the superiority of the multi-layer LSTM model for network packet classification.

Traditional methods of assessing handwritten characters are often too subjective, inefficient, and lagging in feedback, which makes it difficult for educators to achieve fully objective writing assessments and for writers to receive timely suggestions for improvement. In this paper, we propose a convolutional neural network (CNN) architecture that combines the attention mechanism with multi-scale feature fusion; specifically, the features are weighted by designing a bottleneck layer that combines the Squeeze-and-Excitation (SE) attention mechanism to highlight the important information and by applying a multi-scale feature fusion method to enable the network to capture both the global structure and the local details of Chinese characters. Finally, a high-quality dataset containing 26,800 images of handwritten Chinese characters is constructed based on the application scenario of the writing grade test, covering the common Chinese characters in the writing grade exam; The experimental results show that the proposed method achieves 98.6% accuracy on the writing grade exam dataset and 97.05% on the ICDAR-2013 public dataset, significantly improving recognition accuracy. The constructed dataset and improved model are suitable for application scenarios such as writing grade exams, which helps to improve marking efficiency and accuracy.

A massive proliferation of malware variants has posed serious and evolving threats to cybersecurity. Developing intelligent methods to cope with the situation is highly necessary due to the inefficiency of traditional methods. In this paper, a highly efficient, intelligent vision-based malware variants detection method was proposed. Firstly, a bilinear interpolation algorithm was utilized for malware image normalization, and data augmentation was used to resolve the issue of imbalanced malware data sets. Moreover, the paper improved the convolutional neural network (CNN) model by combining multi-scale feature fusion (MFF) and channel attention mechanism for more discriminative and robust feature extraction. Finally, we proposed a hyperparameter optimization algorithm based on the bat algorithm, referred to as HDBA, in order to overcome the disadvantage of the traditional hyperparameter optimization method based on manual adjustment. Experimental results indicated that our model can effectively and efficiently identify malware variants from real and daily networks, with better performance than state-of-the-art solutions.

Deep learning has been applied in the field of network intrusion detection and has yielded good results. In malicious network traffic classification tasks, many studies have achieved good performance with respect to the accuracy and recall rate of classification through self-designed models. In deep learning, the design of the model architecture greatly influences the results. However, the design of the network model architecture usually requires substantial professional knowledge. At present, the focus of research in the field of traffic monitoring is often directed elsewhere. Therefore, in the classification task of the network intrusion detection field, there is much room for improvement in the design and optimization of the model architecture. A neural architecture search (NAS) can automatically search the architecture of the model under the premise of a given optimization goal. For this reason, we propose a model that can perform NAS in the field of network traffic classification and search for the optimal architecture suitable for traffic detection based on the network traffic dataset. Each layer of our depth model is constructed according to the principle of maximum coding rate attenuation, which has strong consistency and symmetry in structure. Compared with some manually designed network architectures, classification indicators, such as Top-1 accuracy and F1 score, are also greatly improved while ensuring the lightweight nature of the model. In addition, we introduce a surrogate model in the search task. Compared to using the traditional NAS model to search the network traffic classification model, our NAS model greatly improves the search efficiency under the premise of ensuring that the results are not substantially different. We also manually adjust some operations in the search space of the architecture search to find a set of model operations that are more suitable for traffic classification. Finally, we apply the searched model to other traffic datasets to verify the universality of the model. Compared with several common network models in the traffic field, the searched model (NAS-Net) performs better, and the classification effect is more accurate.

Convolutional neural network (CNN) has led to significant progress in object detection. In order to detect the objects in various sizes, the object detectors often exploit the hierarchy of the multi-scale feature maps called feature pyramid, which is readily obtained by the CNN architecture. However, such feature maps do not fully consider the supplementary effect of contextual information on semantics. In this work, we proposed a feature fusion method of residual attention based on the SSD benchmark network call Improved SSD to make full use of context information to improve the characterization ability of feature maps. Besides, we use the residual attention mechanism to reinforce the key features to further improve the detector performance. The experiment result on benchmark dataset PASCAL VOC shows that the map of the proposed method with input image sizes of 300×300 and 512×512 is 78.8% and 80.7%.

ObjectiveIn computer-aided medical diagnosis, precise skin lesion segmentation is crucial for the early detection and treatment of skin cancer. However, challenges such as unclear lesion boundaries, low contrast, and varying lesion shapes make accurate segmentation a difficult task. To address these challenges, we propose DMFF-Net, a multi-scale, multi-attention feature fusion network based on DeepLabV3, designed to improve the accuracy of skin lesion segmentation.MethodsDMFF-Net integrates several advanced modules to enhance segmentation performance. The network incorporates a Global Grid Coordinate Attention Module (GGCAM), which effectively fuses spatial and channel features to capture the complex relationships between local and global information. Additionally, a Multi-Scale Depthwise Separable Dilated Convolution (MDSDC) module is employed to strengthen multi-scale feature extraction, thereby preventing resolution degradation during convolution. A Mid-High Level Feature Fusion (MHLFF) module is also introduced to refine critical feature representations and suppress irrelevant information, thereby improving segmentation accuracy.ResultsThe proposed network was evaluated on four publicly available datasets: ISIC 2016, ISIC 2017, ISIC 2018, and PH2. The results show that DMFF-Net significantly outperforms existing advanced methods. Specifically, it achieves MIoU values of 89.31%, 91.47%, and 86.93% on the ISIC 2016, ISIC 2017, and ISIC 2018 datasets, respectively. Furthermore, the network achieves accuracy values of 95.62%, 97.33%, and 94.78%, and F1 scores of 96.93%, 94.91%, and 93.61%, respectively, demonstrating its robustness and effectiveness in skin lesion segmentation.ConclusionThe DMFF-Net model, with its multi-scale feature fusion and attention mechanisms, substantially improves skin lesion segmentation by preserving crucial spatial details and improving feature representation. Its superior performance on multiple datasets highlights its potential as a powerful tool for skin lesion diagnosis and provides an important reference for future advancements in medical image segmentation.

A multi-scale semantic feature fusion method for remote sensing crop classification

Surface Defect Detection Model Combining Multi-scale Feature Fusion and Attention Mechanism

Object tracking is one of the important research topics in the direction of computer vision. And occlusion, deformation and scale change are the most challenging issues in the realm of tracking. In an effort to address the issue that the Siamese network-based object tracking algorithm’s resilience and success rate are both low and weak in complicated circumstances such as similar object interference and object occlusion, a Siamese network Object tracking algorithm combined with attention mechanism based on the existing Siamese network algorithm is proposed. Firstly, in order to extract the features of the template picture and the sample picture, the modified AlexNet convolutional neural network <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</sup> is utilized as the feature extraction network. Secondly, in order to increase the model’s precision and increase tracking’s rate of success, the module for the attention mechanism, which consists of the channel attention mechanism and the spatial attention mechanism, is introduced. The channel attention mechanism can focus on the information useful for tracking the Object, suppress unimportant background information, and the spatial attention mechanism is capable of picking up on the object’s abundant spatial information. Experiments show that the proposed Siamese network target tracking algorithm combined with attention mechanism achieves contestable results on the OTB100 test dataset, and the enhanced method outperforms the original approach in terms of performance.