Search Results (184)

The e-commerce data structure is a typical multiplex graph network structure, which allows multiple types of edges between node pairs. However, existing methods that rely on message-passing frameworks are not sufficient to fully exploit the rich information in multiplex graphs. To improve the performance of link prediction, we propose a novel laminated heterogeneous graph convolutional network (LHGCN) consisting of three core modules: a laminate generation module (LGM), an adaptive convolution module (ACM), and a laminate fusion module (LFM). More specifically, the LGM generates symmetric laminates that cover diverse semantics to create rich node representations. Then, the ACM dynamically adjusts the node receptive field and flexibly captures local information, thereby enhancing the representation ability of the node. Through symmetric information propagation across laminates, the LFM combines multiple laminated features to optimize the global representation, which enables our model to accurately predict links. Moreover, an elaborate loss function, consisting of positive sample loss, negative sample loss, and L2 regularization loss, drives the network to preserve critical information. Extensive experiments on various benchmarks demonstrate the superiority of our method over state-of-the-art alternatives in terms of link prediction. Full article

Pan-sharpening aims to generate high-resolution multispectral (HRMS) images by combining high-resolution panchromatic (PAN) images with low-resolution multispectral (LRMS) data, while maintaining the symmetry of spatial and spectral characteristics. Traditional convolutional neural networks (CNNs) struggle with global dependency modeling due to local receptive fields, and Transformer-based models are computationally expensive. Recent Mamba models offer linear complexity and effective global modeling. However, existing Mamba-based methods lack sensitivity to local feature variations, leading to suboptimal fine-detail preservation. To address this, we propose a Conditional Skipping Mamba Network (CSMN), which enhances global-local feature fusion symmetrically through two modules: (1) the Adaptive Mamba Module (AMM), which improves global perception using adaptive spatial-frequency integration; and (2) the Cross-domain Mamba Module (CDMM), optimizing cross-domain spectral-spatial representation. Experimental results on the IKONOS and WorldView-2 datasets demonstrate that CSMN surpasses existing state-of-the-art methods in achieving superior spectral consistency and preserving spatial details, with performance that is more symmetric in fine-detail preservation. Full article

Despite the successful applications of the remote sensing image in agriculture, meteorology, and geography, its relatively low spatial resolution is hindering the further applications. Super-resolution technology is introduced to conquer such a dilemma. It is a challenging task due to the variations in object size and textures in remote sensing images. To address that problem, we present SymSwin, a super-resolution model based on the Swin transformer aimed to capture a multi-scale context. The symmetric multi-scale window (SyMW) mechanism is proposed and integrated in the backbone, which is capable of perceiving features with various sizes. First, the SyMW mechanism is proposed to capture discriminative contextual features from multi-scale presentations using corresponding attentive window size. Subsequently, a cross-receptive field-adaptive attention (CRAA) module is introduced to model the relations among multi-scale contexts and to realize adaptive fusion. Furthermore, RS data exhibit poor spatial resolution, leading to insufficient visual information when merely spatial supervision is applied. Therefore, a U-shape wavelet transform (UWT) loss is proposed to facilitate the training process from the frequency domain. Extensive experiments demonstrate that our method achieves superior performance in both quantitative metrics and visual quality compared with existing algorithms. Full article

Botnets pose a significant challenge in network security by leveraging Domain Generation Algorithms (DGA) to evade traditional security measures. Extracting DGA domain samples is inherently complex, and the current DGA detection models often struggle to capture domain features effectively when facing limited training data. This limitation results in suboptimal detection performance and an imbalance between model accuracy and complexity. To address these challenges, this paper introduces a novel multi-scale feature fusion model that integrates the Transformer architecture with the Rapid Selective Kernel Network (R-SKNet). The proposed model employs the Transformer’s encoder to couple the single-domain character elements with the multiple types of relationships within the global domain block. This paper proposes integrating R-SKNet into DGA detection and developing an efficient channel attention (ECA) module. By enhancing the branch information guidance in the SKNet architecture, the approach achieves adaptive receptive field selection, multi-scale feature capture, and lightweight yet efficient multi-scale convolution. Moreover, the improved Feature Pyramid Network (FPN) architecture, termed EFAM, is utilized to adjust channel weights for outputs at different stages of the backbone network, leading to achieving multi-scale feature fusion. Experimental results demonstrate that, in tasks with limited training samples, the proposed method achieves lower computational complexity and higher detection accuracy compared to mainstream detection models. Full article

In tasks such as wood defect repair and the production of high-end wooden furniture, ensuring the consistency of the texture in repaired or jointed areas is crucial. This paper proposes the WTSM-SiameseNet model for wood-texture-similarity matching and introduces several improvements to address the issues present in traditional methods. First, to address the issue that fixed receptive fields cannot adapt to textures of different sizes, a multi-receptive field fusion feature extraction network was designed. This allows the model to autonomously select the optimal receptive field, enhancing its flexibility and accuracy when handling wood textures at different scales. Secondly, the interdependencies between layers in traditional serial attention mechanisms limit performance. To address this, a concurrent attention mechanism was designed, which reduces interlayer interference by using a dual-stream parallel structure that enhances the ability to capture features. Furthermore, to overcome the issues of existing feature fusion methods that disrupt spatial structure and lack interpretability, this study proposes a feature fusion method based on feature correlation. This approach not only preserves the spatial structure of texture features but also improves the interpretability and stability of the fused features and the model. Finally, by introducing depthwise separable convolutions, the issue of a large number of model parameters is addressed, significantly improving training efficiency while maintaining model performance. Experiments were conducted using a wood texture similarity dataset consisting of 7588 image pairs. The results show that WTSM-SiameseNet achieved an accuracy of 96.67% on the test set, representing a 12.91% improvement in accuracy and a 14.21% improvement in precision compared to the pre-improved SiameseNet. Compared to CS-SiameseNet, accuracy increased by 2.86%, and precision improved by 6.58%. Full article

Underwater object detection is critical for marine ecological monitoring and biodiversity research, yet existing algorithms struggle in detecting densely packed objects of varying sizes, particularly in occluded and complex underwater environments. This study introduces Underwater-Yolo, a novel detection network that enhances performance in these challenging scenarios by integrating a dual-branch occlusion-handling attention mechanism (GLOAM) and a Cross-Stage Partial Dilated Deformable Convolution (CSP-DDC) backbone. The dilated deformable convolutions (DDCs) in the backbone and neck expand the receptive field, thereby improving the detection of small objects, while the deformable convolutions enhance the model’s adaptive feature extraction capabilities for unstructured objects. Additionally, the CARAFE up-sampling operator in the neck aggregates contextual information across a broader spatial domain. The GLOAM, consisting of a global branch (using a Vision Transformer to capture global features and object–background relationships) and a local branch (enhancing the detection of occluded objects through depthwise–pointwise convolutions), further optimizes performance. By incorporating these innovations, the model effectively addresses the challenges of detecting small and occluded objects in dense underwater environments. The evaluation on the CLfish-V1 dataset shows significant improvements over state-of-the-art algorithms, with an AP50 of 93.8%, an AP75 of 88.9%, and an AP-small of 76.4%, marking gains of 4.7%, 16.7%, and 6%, respectively. These results demonstrate the model’s effectiveness in complex underwater scenarios. Full article

Accurate and efficient tomato detection is one of the key techniques for intelligent automatic picking in the area of precision agriculture. However, under the facility scenario, existing detection algorithms still have challenging problems such as weak feature extraction ability for occlusion conditions and different fruit sizes, low accuracy on edge location, and heavy model parameters. To address these problems, this paper proposed D³-YOLOv10, a lightweight YOLOv10-based detection framework. Initially, a compact dynamic faster network (DyFasterNet) was developed, where multiple adaptive convolution kernels are aggregated to extract local effective features for fruit size adaption. Additionally, the deformable large kernel attention mechanism (D-LKA) was designed for the terminal phase of the neck network by adaptively adjusting the receptive field to focus on irregular tomato deformations and occlusions. Then, to further improve detection boundary accuracy and convergence, a dynamic FM-WIoU regression loss with a scaling factor was proposed. Finally, a knowledge distillation scheme using semantic frequency prompts was developed to optimize the model for lightweight deployment in practical applications. We evaluated the proposed framework using a self-made tomato dataset and designed a two-stage category balancing method based on diffusion models to address the sample class-imbalanced issue. The experimental results demonstrated that the D³-YOLOv10 model achieved an $mA{P}_{0.5}$ of 91.8%, with a substantial reduction of 54.0% in parameters and 64.9% in FLOPs, compared to the benchmark model. Meanwhile, the detection speed of 80.1 FPS more effectively meets the demand for real-time tomato detection. This study can effectively contribute to the advancement of smart agriculture research on the detection of fruit targets. Full article

The segmentation and recognition of power lines are crucial for the UAV-based inspection of overhead power lines. To address the issues of class imbalance, low sample quantity, and long-range dependency in images, a specialized semantic segmentation network for power line segmentation called Axial-UNet++ is proposed. Firstly, to tackle the issue of long-range dependencies in images and low sample quantity, a gated axial attention mechanism is introduced to expand the receptive field and improve the capture of relative positional biases in small datasets, thereby proposing a novel feature extraction module termed axial-channel local normalization module. Secondly, to address the imbalance in training samples, a new loss function is developed by combining traditional binary cross-entropy loss with focal loss, enhancing the precision of image semantic segmentation. Lastly, ablation and comparative experiments on the PLDU and Mendeley datasets demonstrate that the proposed model achieves 54.7% IoU and 80.1% recall on the PLDU dataset, and 79.3% IoU and 93.1% recall on the Mendeley dataset, outperforming other listed models. Additionally, robustness experiments show the adaptability of the Axial-UNet++ model under extreme conditions and the augmented image dataset used in this study has been open sourced. Full article

Precise weed recognition is an important step towards achieving intelligent agriculture. In this paper, a novel weed recognition model, Cotton Weed-YOLO, is proposed to improve the accuracy and efficiency of weed detection. CW-YOLO is based on YOLOv8 and introduces a dual-branch structure combining a Vision Transformer and a Convolutional Neural Network to address the problems of the small receptive field of the CNN and the high computational complexity of the transformer. The Receptive Field Enhancement (RFE) module is proposed to enable the feature pyramid network to adapt to the feature information of different receptive fields. A Scale-Invariant Shared Convolutional Detection (SSCD) head is proposed to fully utilize the advantages of shared convolution and significantly reduce the number of parameters in the detection head. The experimental results show that the CW-YOLO model outperforms existing methods in terms of detection accuracy and speed. Compared with the original YOLOv8n, the detection accuracy, mAP value, and recall rate are improved by 1.45, 0.7, and 0.6%, respectively, the floating-point numbers are reduced by 2.5 G, and the number of parameters is reduced by 1.52 × 10⁶ times. The proposed CW-YOLO model provides powerful technical support for smart agriculture and is expected to promote the development of agricultural production in the direction of intelligence and precision. Full article

Aiming at the problem that the existing human skeleton behavior recognition methods are insensitive to human local movements and show inaccurate recognition in distinguishing similar behaviors, a multi-scale spatio-temporal graph convolution method incorporating multi-granularity features is proposed for human behavior recognition. Firstly, a skeleton fine-grained partitioning strategy is proposed, which initializes the skeleton data into data streams of different granularities. An adaptive cross-scale feature fusion layer is designed using a normalized Gaussian function to perform feature fusion among different granularities, guiding the model to focus on discriminative feature representations among similar behaviors through fine-grained features. Secondly, a sparse multi-scale adjacency matrix is introduced to solve the bias weighting problem that amplifies the multi-scale spatial domain modeling process under multi-granularity conditions. Finally, an end-to-end graph convolutional neural network is constructed to improve the feature expression ability of spatio-temporal receptive field information and enhance the robustness of recognition between similar behaviors. The feasibility of the proposed algorithm was verified on the public behavior recognition dataset MSR Action 3D, with a accuracy of 95.67%, which is superior to existing behavior recognition methods. Full article

Deep learning, a foundational technology in artificial intelligence, facilitates the identification of complex associations between stock prices and various influential factors through comprehensive data analysis. Stock price data exhibits unique time-series characteristics; models emphasizing long-term data may miss short-term fluctuations, while those focusing solely on short-term data may not capture cyclical trends. Existing models that integrate long short-term memory (LSTM) and convolutional neural networks (CNNs) face limitations in capturing both long- and short-term dependencies due to LSTM’s gated transmission mechanism and CNNs’ limited receptive field. This study introduces an innovative deep learning model, CNN-CBAM-LSTM, which integrates the convolutional block attention module (CBAM) to enhance the extraction of both long- and short-term features. The model’s performance is assessed using the Australian Standard & Poor’s 200 Index (AS51), showing improvement over traditional models across metrics such as RMSE, MAE, R², and RETURN. To further confirm its robustness and generalizability, Diebold–Mariano (DM) tests and model confidence set experiments are conducted, with results indicating the consistently high performance of the CNN-CBAM-LSTM model. Additional tests on six globally recognized stock indices reinforce the model’s predictive strength and adaptability, establishing it as a reliable tool for forecasting in the stock market. Full article

The precise extraction of building footprints using remote sensing technology is increasingly critical for urban planning and development amid growing urbanization. However, considering the complexity of building backgrounds, diverse scales, and varied appearances, accurately and efficiently extracting building footprints from various remote sensing images remains a significant challenge. In this paper, we propose a novel network architecture called ME-FCN, specifically designed to perceive and optimize multi-scale features to effectively address the challenge of extracting building footprints from complex remote sensing images. We introduce a Squeeze-and-Excitation U-Block (SEUB), which cascades multi-scale semantic information exploration in shallow feature maps and incorporates channel attention to optimize features. In the network’s deeper layers, we implement an Adaptive Multi-scale feature Enhancement Block (AMEB), which captures large receptive field information through concatenated atrous convolutions. Additionally, we develop a novel Dual Multi-scale Attention (DMSA) mechanism to further enhance the accuracy of cascaded features. DMSA captures multi-scale semantic features across both channel and spatial dimensions, suppresses redundant information, and realizes multi-scale feature interaction and fusion, thereby improving the overall accuracy and efficiency. Comprehensive experiments on three datasets demonstrate that ME-FCN outperforms mainstream segmentation methods. Full article

With the development of unmanned aerial vehicle (UAV) technology, deep learning is becoming more and more widely used in object detection in UAV aerial images; however, detecting and identifying small objects in low-illumination scenes is still a major challenge. Aiming at the problem of low brightness, high noise, and obscure details of low-illumination images, an object detection algorithm, LI-YOLO (Low-Illumination You Only Look Once), for UAV aerial images in low-illumination scenes is proposed. Specifically, in the feature extraction section, this paper proposes a feature enhancement block (FEB) to realize global receptive field and context information learning through lightweight operations and embeds it into the C2f module at the end of the backbone network to alleviate the problems of high noise and detail blur caused by low illumination with very few parameter costs. In the feature fusion part, aiming to improve the detection performance for small objects in UAV aerial images, a shallow feature fusion network and a small object detection head are added. In addition, the adaptive spatial feature fusion structure (ASFF) is also introduced, which adaptively fuses information from different levels of feature maps by optimizing the feature fusion strategy so that the network can more accurately identify and locate objects of various scales. The experimental results show that the mAP50 of LI-YOLO reaches 76.6% on the DroneVehicle dataset and 90.8% on the LLVIP dataset. Compared with other current algorithms, LI-YOLO improves the mAP 50 by 3.1% on the DroneVehicle dataset and 6.9% on the LLVIP dataset. Experimental results show that the proposed algorithm can effectively improve object detection performance in low-illumination scenes. Full article

Coastal aquaculture plays a crucial role in global food security and the economic development of coastal regions, but it also causes environmental degradation in coastal ecosystems. Therefore, the automation, accurate extraction, and monitoring of coastal aquaculture areas are crucial for the scientific management of coastal ecological zones. This study proposes a novel deep learning- and attention-based median adaptive fusion U-Net (MAFU-Net) procedure aimed at precisely extracting individually separable aquaculture ponds (ISAPs) from medium-resolution remote sensing imagery. Initially, this study analyzes the spectral differences between aquaculture ponds and interfering objects such as saltwater fields in four typical aquaculture areas along the coast of Liaoning Province, China. It innovatively introduces a difference index for saltwater field aquaculture zones (DIAS) and integrates this index as a new band into remote sensing imagery to increase the expressiveness of features. A median augmented adaptive fusion module (MEA-FM), which adaptively selects channel receptive fields at various scales, integrates the information between channels, and captures multiscale spatial information to achieve improved extraction accuracy, is subsequently designed. Experimental and comparative results reveal that the proposed MAFU-Net method achieves an F1 score of 90.67% and an intersection over union (IoU) of 83.93% on the CHN-LN4-ISAPS-9 dataset, outperforming advanced methods such as U-Net, DeepLabV3+, SegNet, PSPNet, SKNet, UPS-Net, and SegFormer. This study’s results provide accurate data support for the scientific management of aquaculture areas, and the proposed MAFU-Net method provides an effective method for semantic segmentation tasks based on medium-resolution remote sensing images. Full article

Seismic data processing plays a pivotal role in extracting valuable subsurface information for various geophysical applications. However, seismic records often suffer from inherent random noise, which obscures meaningful geological features and reduces the reliability of interpretations. In recent years, deep learning methodologies have shown promising results in performing noise attenuation tasks on seismic data. In this research, we propose modifications to the standard U-Net structure by integrating dense and residual connections, which serve as the foundation of our approach named the dense and residual (DARE U-Net) network. Dense connections enhance the receptive field and ensure that information from different scales is considered during the denoising process. Our model implements local residual connections between layers within the encoder, which allows earlier layers to directly connect with deep layers. This promotes the flow of information, allowing the network to utilize filtered and unfiltered input. The combined network mechanisms preserve the spatial information loss during the contraction process so that the decoder can locate the features more accurately by retaining the high-resolution features, enabling precise location in seismic image denoising. We evaluate this adapted architecture by applying synthetic and real data sets and calculating the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM). The effectiveness of this method is well noted. Full article