Search Results (2,037)

In recent years, rapid and reliable interpretation for emergency response to landslides and other geological hazards has become increasingly important. This paper presents DFmamba, an improved deformable dual-branch visual state-space network, to address engineering challenges such as missed large landslide bodies, boundary shifts, and loss of small-scale details. DFmamba mitigates the limited effective receptive field and window-partition constraints that often prevent existing methods from balancing large-area semantic consistency, multi-scale detection, precise boundary delineation, and computational efficiency. It employs a parallel encoder with a convolutional branch and a Visual State-Space Model (VSSM) branch to jointly capture local textures and global context. In the decoder, deformable residual blocks (DRB) enhance geometric modeling of irregular boundaries, while multi-scale feature alignment and a shallow high-frequency injection (MFP) mechanism strengthen boundary responses and preserve fine details. Experiments on the public CAS dataset against representative CNN-, Transformer-, and SSM-based baselines show that DFmamba achieves improved Precision, Recall, F1-score, and IoU, with stable performance across multi-scale scenarios, demonstrating strong robustness for landslide segmentation. Full article

Accurate photovoltaic (PV) power forecasting is essential for the reliable integration of renewable energy into electrical grids. This paper proposes a novel Multi-Scale CNN-Transformer network with Residual Correction (MSCT-RCM) for ultra-short-term PV power forecasting. The model integrates parallel multi-scale convolutional neural networks (CNNs) to extract local temporal features, a Transformer encoder to capture long-range dependencies, and a Residual Correction Module (RCM) that dynamically refines predictions using historical error patterns. A two-stage training strategy is employed to stabilize learning and enhance performance. Experimental evaluation on two years of operational data from a large-scale PV plant demonstrates that the proposed model achieves an R² value of 0.9944 for 15-minute-ahead forecasts and reduces mean absolute error (MAE) and root mean square error (RMSE) by over 50% in one-hour-ahead predictions compared to benchmark models. The MSCT-RCM model therefore exhibits strong potential for deployment in scenarios requiring high-precision predictions, such as smart grid scheduling. Full article

Hyperspectral images (HSIs) from the lunar polar regions are frequently compromised by low signal-to-noise ratio (SNR) under adverse illumination, limiting their utility for scientific analysis. Existing spectral-only compensation approaches operate without spatial context, leading to speckle-like artifacts that degrade spatial consistency and constrain subsequent applications. To address this limitation, we propose SSF-3DSC, a spatial–spectral fusion 3D signal-compensation framework tailored for lunar HSIs to simultaneously restore spectral fidelity and spatial consistency under extreme low-illumination conditions. To the best of our knowledge, this represents the first deep learning framework specifically engineered for joint spatial–spectral restoration in the photon-starved regime. SSF-3DSC integrates three specialized components: a spectral compensation module (SCM) for restoring spectral fidelity, a multi-scale spatial attention (MSA) module for capturing hierarchical spatial patterns, and a cascaded 3D residual convolutional module (C3D-RCM) for refining spatial–spectral representations. Trained on paired low- and high-SNR Moon Mineralogy Mapper (M3) data cubes from the lunar south polar region, SSF-3DSC employs synergistic spatial–spectral fusion to achieve high-fidelity reconstruction, significantly outperforming a spectral-only lunar baseline (Paired-CycleGAN). Regional-scale experiments demonstrate its ability to recover both spatially coherent geological structures and spectrally reliable mineral abundance maps. By establishing a new benchmark for lunar HSI restoration under low-illumination conditions, this work enhances the scientific utility of low-signal M3 data and enables robust quantitative investigations into the Moon’s challenging polar regions. Full article

Intrusion detection aims to effectively detect abnormal attacks in Internet of Things (IoT) networks, which is crucial for cybersecurity. However, it is difficult for traditional intrusion detection methods to effectively extract data features from traffic data, and most existing models are too complex to be deployed on edge servers. Addressing this need, this paper proposes a hybrid feature selection method and a lightweight deep learning intrusion detection model. Firstly, the data feature space is reduced using variance filtering, mutual information, and the Pearson Correlation Coefficient, thereby reducing the computational cost of subsequent model training. Then, an intrusion detection model based on a Temporal Convolutional Network (TCN) is constructed. This model utilizes dilated causal convolutions to effectively capture long-term temporal dependencies in network traffic. Simultaneously, the residual connections are used to mitigate the vanishing gradient problem, making the model easier to train and converge. Finally, experiments are conducted on the newly released Edge-IIoTset dataset. The results show that the proposed feature selection algorithm maintains good detection performance despite a significant reduction in feature dimensionality. Furthermore, compared with other models, the proposed TCN-based approach achieves higher classification accuracy with lower computational overhead, demonstrating its suitability for deployment in resource-constrained edge computing environments. Full article

Object detection in UAV aerial imagery plays a pivotal role across a wide spectrum of applications. However, existing detection models continue to face significant challenges stemming from small object scales, dense spatial distributions, and highly complex backgrounds. To address these challenges, this paper proposes a novel dual-backbone network model named WCDB-YOLO. The core innovation of this work lies in introducing a “target-context decoupled perception” paradigm, which utilizes two structurally complementary backbone networks to separately process local object features and global background information: one backbone focuses on extracting fine-grained local features of objects, while the other innovatively incorporates a wavelet convolution module to efficiently model the global contextual semantics of complex scenes with minimal computational cost by constructing a large receptive field. To further enhance the scale adaptability for small objects, a Dilation-wise Residual (DWR) module is designed, which employs parallel convolutional branches with different dilation rates to achieve dynamic adaptation to multi-scale small object features. Additionally, the model optimizes the feature pyramid structure by integrating high-resolution P2/4 features into the detection head, significantly improving the localization accuracy of tiny objects. Experimental results on the VisDrone dataset show that the proposed method achieves an 8.4% improvement in mAP50 over the baseline YOLOv11s model and outperforms current state-of-the-art (SOTA) approaches. This work presents a highly accurate and robust solution for small object detection from UAV platforms in complex environments. Full article

During high-speed cornering, the motion accuracy and efficiency of SCARA robots are often compromised by residual vibrations and cornering errors. Conventional control methods often fail to address these two coupled problems simultaneously. Therefore, this study developed an integrated design strategy to simultaneously suppress residual vibrations and restrict cornering errors for improving the cornering performance of the SCARA robot. The core of this design strategy is to develop a hybrid prefilter via the convolution of an input shaper and a finite impulse response filter, thereby creating a prefilter with robust, high-performance residual vibration suppression. Subsequently, to accommodate the asymmetric acceleration and deceleration generated by the hybrid prefilter, this study developed a systematic corner trajectory planning method that can calculate the cornering trajectory parameters based on a preset value of the cornering error to restrict the cornering error and ensure the cornering accuracy of the SCARA robot. Experimental results indicated that under the condition of a restricted cornering error, the developed hybrid prefilter can reduce residual vibration by >85%. Thus, the hybrid prefilter designed with the corner trajectory planning method can mitigate the coupled problem of residual vibration and cornering error, suppressing the residual vibration without compromising cornering accuracy. Full article

Space-based automatic dependent surveillance–broadcast (ADS-B) systems offer the potential for comprehensive global aircraft surveillance. However, they face substantial challenges due to severe signal collisions resulting from the simultaneous reception of asynchronous ADS-B transmissions from multiple aircraft within a satellite’s expansive coverage area. Traditional collision mitigation approaches, such as serial interference cancellation and multichannel blind source separation, often have high computational costs, impose strict signal structure constraints, or rely on multiple-antenna configurations, all of which limit their practicality in satellite scenarios. To address these limitations, this paper proposes two novel deep learning–based models, designated SplitNet-2 and SplitNet-3. SplitNet-2 leverages a Transformer-inspired self-attention architecture specifically designed to separate two overlapping ADS-B signals, while SplitNet-3 employs a convolutional residual U-shaped network optimized for disentangling three simultaneous, colliding signals. Extensive simulations under realistic satellite reception conditions demonstrate that the proposed models significantly outperform conventional methods, achieving lower bit error rates (BERs) and improved demodulation accuracy. These advancements offer a promising solution to the critical problem of underdetermined signal separation in space-based ADS-B reception and significantly enhance the reliability and coverage of satellite-based ADS-B surveillance systems. Full article

Single image super-resolution (SISR) is a classical computer vision task that aims to reconstruct a high-resolution image from a low-resolution input, thereby improving detail sharpness and visual quality. In recent years, convolutional neural network (CNN)-based methods and transformer-based methods using self-attention mechanisms have achieved significant progress in visible-image super-resolution. However, the direct application of these two types of methods to infrared images still poses considerable challenges. On the one hand, infrared images generally suffer from low signal-to-noise ratio, blurred edges, and missing details, and relying only on local convolutions makes it difficult to adequately model long-range dependencies across regions. On the other hand, although pure transformer models have a strong global modeling ability, they usually have large numbers of parameters and are sensitive to the amount of training data, making it difficult to balance efficiency and detail restoration in infrared imaging scenarios. To address these issues, we propose a hybrid neural network architecture for infrared image super-resolution reconstruction, termed RDSR (Residual Dual-branch Separable Super-Resolution Network), which organically integrates multi-scale depthwise separable convolutions with shifted-window self-attention. Specifically, we design a dual-branch spatial interaction module (BDSI, Dual-Branch Spatial Interaction) and a multi-scale separable spatial aggregation module (MSSA, Multi-Scale Separable Spatial Aggregation). The BDSI module models correlations along rows and columns through grouped convolutions in the horizontal and vertical directions, effectively strengthening the spatial information interaction between the convolution branch and the self-attention branch. The MSSA module replaces the conventional MLP with three parallel depthwise separable convolution branches, improving the feature representation and nonlinear modeling through multi-scale spatial aggregation and a star-shaped gating operation. The experimental results on multiple public infrared image datasets show that for $\times 2$ and $\times 4$ upscaling, the proposed RDSR achieves higher PSNR and SSIM values than CNN-based methods such as EDSR, RCAN, and RDN, as well as transformer-based methods such as SwinIR, DAT, and HAT, demonstrating the effectiveness of the proposed modules and the overall framework. Full article

Underwater images are frequently degraded by wavelength-dependent absorption and scattering, which introduce strong color casts, reduce contrast, and obscure fine structures. Although learning-based enhancement methods have recently improved perceptual quality, many remain computationally intensive, limiting deployment on resource-constrained underwater platforms. To address this challenge, we propose LCS-Net, a lightweight framework for single underwater image enhancement that targets a favorable quality–efficiency trade-off. LCS-Net first applies a dynamic Learnable Color Correction Module (LCCM) that predicts image-specific correction parameters from global color statistics, enabling low-overhead cast compensation and stabilizing the input distribution. Feature extraction is conducted using efficient inverted residual blocks equipped with squeeze-and-excitation (SE) to recalibrate channel responses and facilitate detail recovery under scattering-induced degradation. At the bottleneck, a Selective Multi-Scale Dilated Block (SMSDB) aggregates complementary context via parallel dilated convolutions and global cues and adaptively reweights the fused features to handle diverse water conditions. Extensive experiments on public benchmarks demonstrate that LCS-Net achieves competitive performance, yielding a PSNR of 26.46 dB and an SSIM of 0.92 on UIEB, along with 28.71 dB and 0.86 on EUVP, while maintaining a compact model size and low computational cost, highlighting its potential for practical deployment. Full article

This study introduces a hybrid point cloud compression method that transfers from octree-nodes to voxel occupancy estimation to find its lower-bound bitrate by using a Binary Arithmetic Range Coder. In previous attempts, we demonstrated that our entropy compression model based on index convolution achieves promising performance while maintaining low complexity. However, our previous model lacks an autoregressive approach, which is apparently indispensable to compete with the current state-of-the-art of compression performance. Therefore, we adapt an autoregressive grouping method that iteratively populates, explores, and estimates the occupancy of 1-bit voxel candidates in a more discrete fashion. Furthermore, we refactored our backbone architecture by adding a distiller layer on each convolution, forcing every hidden feature to contribute to the final output. Our proposed model extracts local features using lightweight 1D convolution applied in varied ordering and analyzes causal relationships by optimizing the cross-entropy. This approach efficiently replaces the voxel convolution techniques and attention models used in previous works, providing significant improvements in both time and memory consumption. The effectiveness of our model is demonstrated on three datasets, where it outperforms recent deep learning-based compression models in this field. Full article

This paper introduces a hybrid two-stage registration framework for reconstructing three-dimensional (3D) kidney anatomy from macroscopic slices, using CT-derived models as the geometric reference standard. The approach addresses the data-scarcity and high-distortion challenges typical of macroscopic imaging, where fully learning-based registration (e.g., VoxelMorph) often fails to generalize due to limited training diversity and large nonrigid deformations that exceed the capture range of unconstrained convolutional filters. In the proposed pipeline, the Optimal Cross-section Matching (OCM) algorithm first performs constrained global alignment—translation, rotation, and uniform scaling—to establish anatomically consistent slice initialization. Next, a lightweight deep-learning refinement network, inspired by VoxelMorph, predicts residual local deformations between consecutive slices. The core novelty of this architecture lies in its hierarchical decomposition of the registration manifold: the OCM acts as a deterministic geometric anchor that neutralizes high-amplitude variance, thereby constraining the learning task to a low-dimensional residual manifold. This hybrid OCM + DL design integrates explicit geometric priors with the flexible learning capacity of neural networks, ensuring stable optimization and plausible deformation fields even with few training examples. Experiments on an original dataset of 40 kidneys demonstrated that the OCM + DL method achieved the highest registration accuracy across all evaluated metrics: NCC = 0.91, SSIM = 0.81, Dice = 0.90, IoU = 0.81, HD95 = 1.9 mm, and volumetric agreement ${\mathrm{DC}}_{\mathrm{Vol}}$ = 0.89. Compared to single-stage baselines, this represents an average improvement of approximately 17% over DL-only and 14% over OCM-only, validating the synergistic contribution of the proposed hybrid strategy over standalone iterative or data-driven methods. The pipeline maintains physical calibration via Hough-based grid detection and employs Bézier-based contour smoothing for robust meshing and volume estimation. Although validated on kidney data, the proposed framework generalizes to other soft-tissue organs reconstructed from optical or photographic cross-sections. By decoupling interpretable global optimization from data-efficient deep refinement, the method advances the precision, reproducibility, and anatomical realism of multimodal 3D reconstructions for surgical planning, morphological assessment, and medical education. Full article

Traffic flow prediction is a pivotal task in intelligent transportation systems. The primary challenge lies in accurately modeling the dynamically evolving and directional spatio-temporal dependencies inherent in road networks. Existing graph neural network-based methods suffer from three main limitations: (1) symmetric adjacency matrices fail to capture the causal propagation of traffic flow from upstream to downstream; (2) the serial combination of graph and temporal convolutions lacks an explicit modeling of joint spatio-temporal state transition probabilities; (3) the inherent low-pass filtering property of temporal convolutional networks tends to smooth high-frequency abrupt signals, thereby weakening responsiveness to sudden events. To address these issues, this paper proposes a causal-enhanced spatio-temporal Markov graph convolutional network (CSHGCN). At the spatial modeling level, we construct an asymmetric causal adjacency matrix by decoupling source and target node embeddings to learn directional traffic flow influences. At the spatio-temporal joint modeling level, we design a spatio-temporal Markov transition module (STMTM) based on spatio-temporal Markov chain theory, which explicitly learns conditional transition patterns through temporal dependency encoders, spatial dependency encoders, and a joint transition network. At the temporal modeling level, we introduce differential feature enhancement and high-frequency residual compensation mechanisms to preserve key abrupt change information through frequency-domain complementarity. Experiments on four datasets—PEMS03, PEMS04, PEMS07, and PEMS08—demonstrate that CSHGCN outperforms existing baselines in terms of MAE, RMSE, and MAPE, with ablation studies validating the effectiveness of each module. Full article

Real-time lithology identification during drilling faces challenges such as indistinct boundaries and difficulties in feature extraction. To address these, this study proposes the MSC-Transformer, a novel model integrating time-frequency features with a deep neural network. A series of drilling experiments were conducted using an intelligent drilling platform, during which triaxial vibration signals were collected from five types of rock specimens: anthracite, granite, bituminous coal, sandstone, and shale. Short-time Fourier Transform (STFT) was applied to generate multi-channel power spectral density (PSD) maps, which were then fused into a three-channel tensor to preserve directional frequency information and used as inputs to the model. The proposed MSC-Transformer combines a multi-scale convolutional (MSC) module with a lightweight Transformer encoder to jointly capture local texture patterns and global dependency features, thereby enabling accurate classification of complex lithologies. Experimental results demonstrate that the model achieves an average accuracy of 98.21 ± 0.49% on the test set, outperforming convolutional neural networks (CNNs), visual geometry group (VGG), residual network (ResNet), and bidirectional long short-term memory (Bi-LSTM) by 5.93 ± 0.90%, 2.54 ± 1.11%, 6.38 ± 2.63%, and 10.56 ± 3.11%, respectively, with statistically significant improvements (p < 0.05). Ablation studies and visualization analyses further validate the effectiveness and interpretability of the model architecture. These findings indicate that lithology recognition based on time-frequency representations of vibration signals is both stable and generalizable, offering technical support for real-time intelligent lithology identification during drilling operations. Full article

Destructive earthquakes frequently sever transportation lifelines, significantly impeding the progress of emergency rescue and post-disaster reconstruction efforts. The automated identification of road damage utilizing high-resolution remote sensing imagery is strictly constrained by the scarcity of post-disaster labeled samples and the morphological complexity of road networks. Consequently, model segmentation results frequently suffer from discontinuities in topological connectivity and confusion between background features and damaged roads. To address these challenges, this study proposes a road damage detection framework that integrates generative artificial intelligence with vector prior knowledge. A data simulation pipeline utilizing a stable diffusion model was constructed, employing topologically constrained masking to generate high-fidelity synthetic damage samples based on the DeepGlobe dataset, thereby mitigating the data deficit. The proposed Vector-Guided Damaged Road Segmentation Network (VRD-U2Net) employs wavelet convolutions (WTConv) to decouple high-frequency noise from low-frequency structural components and utilizes a Multi-Scale Residual Attention (MSRA) module to align visual features with vector priors. Furthermore, a vector-prior-driven dynamic upsampling mechanism is introduced to enforce geometric constraints on model predictions. Experimental results demonstrate that the method achieves an mIoU of 0.884 on the synthetic dataset. In validation using real-world imagery from the 2023 Turkey earthquake, the model attained an F1-score of 65.3% and recall of 72.3% without fine-tuning, exhibiting robust generalization capabilities to support manual damage assessment in data-scarce emergency scenarios. Full article

To address critical limitations in conventional forward collision warning (FCW) systems including inadequate road condition detection accuracy, significant warning area prediction errors, and poor environmental adaptability on wet/snow-covered roads, this study develops an adaptive collision warning framework based on real-time road slippery states recognition. An enhanced ED-ResNet50 model is proposed, incorporating grouped convolutions within the backbone network and embedding ECA attention mechanisms after the second/third residual blocks alongside DDS-DA modules after the fourth block, significantly improving discriminative capability for pavement texture analysis under adverse conditions. This vision-based recognition system synchronizes with YOLOv8 for preceding vehicle detection, enabling the construction of a friction-sensitive safety distance and the time-to-collision model that dynamically calibrates warning thresholds according to instantaneous vehicle velocity and road adhesion coefficients. Real-vehicle validation demonstrates an 8.76% improvement in overall warning accuracy and 7.29% reduction in lateral and early false alarm rates compared to static-threshold systems, confirming practical efficacy for safety assurance in inclement weather. Full article