Search Results (4,374)

Ensuring worker safety compliance and secure cargo transportation in complex port environments is critical for modern logistics hubs. However, conventional supervision methods, including manual inspection and passive video monitoring, suffer from limited coverage, poor real-time responsiveness, and low robustness under frequent occlusion, scale variation, and cross-camera transitions, leading to unstable target association and missed risk events. To address these challenges, this paper proposes CSPC-BRS, a real-time multi-object detection and tracking framework for open-channel port scenarios. CSPC (Coordinated Spatial Perception Cascade) enhances the YOLOv8 backbone by integrating CASAM, SPPELAN-DW, and CACC modules to improve feature representation under cluttered backgrounds and degraded visual conditions. Meanwhile, BRS (Bounding Box Reduction Strategy) mitigates scale distortion during tracking, and a Multi-Dimensional Re-identification Scoring (MDRS) mechanism fuses six perceptual features—color, texture, shape, motion, size, and time—to achieve stable cross-camera identity consistency. Experimental results demonstrate that CSPC-BRS outperforms the YOLOv8-n baseline by improving the mAP@0.5:0.95 by 9.6% while achieving a real-time speed of 132.63 FPS. Furthermore, in practical deployment, it reduces the false capture rate by an average of 59.7% compared to the YOLOv8 + Bot-SORT tracker. These results confirm that CSPC-BRS effectively balances detection accuracy and computational efficiency, providing a practical and deployable solution for intelligent safety monitoring in complex industrial logistics environments. Full article

This paper presents a cascaded imaging method that combines low-light enhancement and visible–long-wavelength infrared (VIS-LWIR) image fusion to mitigate image degradation in dark environments. The framework incorporates a Low-Light Enhancer Network (LLENet) for improving visible image illumination and a heterogeneous information fusion subnetwork (IXNet) for integrating features from enhanced VIS and LWIR images. Using a joint training strategy with a customized loss function, the approach effectively preserves salient targets and texture details. Experimental results on the LLVIP, M³FD, TNO, and MSRS datasets demonstrate that the method produces high-quality fused images with superior performance evaluated by quantitative metrics. It also exhibits excellent generalization ability, maintains a compact model size with low computational complexity, and significantly enhances performance in high-level visual tasks like object detection, particularly in challenging low-light scenarios. Full article

Visual emotion recognition in unconstrained environments remains challenging, as single-stream deep learning models often fail to capture the localized facial cues and contextual information necessary for accurate classification. This study introduces a hierarchical multi-level feature fusion framework that systematically combines low-level micro-textural features (Local Binary Patterns), mid-level facial cues (Facial Action Units), and high-level scene semantics (Places365) with ResNet-50 global embeddings. Evaluated on the large-scale EmoSet-3.3M dataset, which contains 3.3 million images across eight emotion categories, the framework demonstrates marked performance gains with the best configuration (LBP-FAUs-Places365-ResNet). The proposed framework achieves 74% accuracy and a macro-averaged F1-score of 0.75 under its best configuration (LBP-FAUs-Places365-ResNet), representing a five-percentage-point improvement over the ResNet-50 baseline. The approach excels at distinguishing high-intensity emotions, maintaining efficient inference (2.2 ms per image, 29 M parameters), and analysis confirms that integrating facial muscle activations with scene context enables nuanced emotional differentiation. These results validate that hierarchical feature integration significantly advances robust, human-aligned visual emotion recognition, making it suitable for real-world Human–Computer Interaction (HCI) and affective computing applications. Full article

Magmatic zircon trace element compositions and their variation trends provide valuable insights into the nature and evolutionary processes of magmatic rocks. The Himalayan orogen contains widespread leucogranites. Despite extensive studies on these granites, the features and petrogenetic implications of trace element composition of zircons from the leucogranites remain poorly constrained. In this study, we present a comprehensive dataset comprising new cathodoluminescence (CL) images, U-Pb ages, and trace element compositions of zircons from the Himalayan leucogranites, and compare them to the previously reported trace element data of zircon from I-type granites. Our results show that zircons from the Himalayan leucogranites have high Hf, U, Y, P, Th, Sc, and heavy rare earth element contents (HREE), and low Nb, Ta, Ti, and light rare earth element contents (LREE), and can be divided into two types. Type I (low-U) zircons exhibit well-developed oscillatory zoning, and the U concentrations are mostly <5000 ppm. Type II (high-U) zircons display mottled or spongy textures and possess elevated U contents that are mostly >5000 ppm. Zircons from the Himalayan leucogranites have higher contents of U, Hf, Nb, Ta, and elevated U/Yb ratios, but lower Th/U, Eu/Eu*, Ce/Ce*, LREE/HREE, and Ce/U values than those from I-type granitic zircons. Furthermore, zircons in the Himalayan leucogranites have gradually decreasing Th, Ti, Th/U, Eu/Eu*, and Ce/Ce*, and increasing U, Nb, Ta, and (Yb/Gd)_N with increasing Hf. These geochemical features suggest the magmas involved in the genesis of leucogranites originated from the partial melting of metasedimentary sources under relatively reduced conditions, and underwent a high degree of magmatic fractionation. Full article

Accurate 3D medical image segmentation is crucial for knowledge-driven clinical decision-making and computer-aided diagnosis. However, current deep learning methods often fail to effectively integrate local structural details from Convolutional Neural Networks (CNNs) with global semantic context from Transformers due to semantic inconsistency and poor cross-scale feature alignment. To address this, Progressive Query Aggregation Network (PQAN), a novel framework that incorporates knowledge-guided feature interaction mechanisms, is proposed. PQAN employs two complementary query modules: Structural Feature Query, which uses anatomical morphology for boundary-aware representation, and Content Feature Query, which enhances semantic alignment between encoding and decoding stages. To enhance texture perception, a Texture Attention (TA) module based on Sobel operators adds directional edge awareness and fine-detail enhancement. Moreover, a Progressive Aggregation Strategy with Forward and Backward Cross-Stage Attention gradually aligns and refines multi-scale features, thereby reducing semantic deviations during CNN-Transformer fusion. Experiments on public benchmarks demonstrate that PQAN outperforms state-of-the-art models in both global accuracy and boundary segmentation. On the BTCV and FLARE datasets, PQAN had average Dice scores of 0.926 and 0.816, respectively. These results demonstrate PQAN’s ability to capture complex anatomical structures, small targets, and ambiguous organ boundaries, resulting in an interpretable and scalable solution for real-world clinical deployment. Full article

Lung and colon cancers remain among the leading causes of cancer-related mortality worldwide, underscoring the need for rapid and accurate histopathological diagnosis. Manual examination of biopsy slides is often time-consuming and prone to inter-observer variability, which highlights the importance of developing reliable and explainable automated diagnostic systems. This study presents DPCSE-Net, a lightweight dual-path convolutional neural network enhanced with a squeeze-and-excitation (SE) attention mechanism for lung and colon cancer classification. The dual-path structure captures both fine-grained cellular textures and global contextual information through multiscale feature extraction, while the SE attention module adaptively recalibrates channel responses to emphasize discriminative features. To enhance transparency and interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM), attention heatmaps, and Integrated Gradients are employed to visualize class-specific activation patterns and verify that the model’s focus aligns with diagnostically relevant tissue regions. Evaluated on the publicly available LC25000 dataset, DPCSE-Net achieved state-of-the-art performance with 99.88% accuracy and F1-score, while maintaining low computational complexity. Ablation experiments confirmed the contribution of the dual-path design and SE module, and qualitative analyses demonstrated the model’s strong interpretability. These results establish DPCSE-Net as an accurate, efficient, and explainable framework for computer-aided histopathological diagnosis, supporting the broader goals of explainable AI in computer vision. Full article

AgI and ZnI₂ nanocrystals are key components for AgI-ZnI₂-SiO₂ hybrid powders (HPs), which could be potentially important for atmospheric artificial precipitation technology. HPs were created by the “Hydrothermal template cocondensation” method (“HTC” method). Mesoporous silica dioxide (MCM48, MCM41, SBA15, SBA16), silver iodides, and zinc iodides were simultaneously grown under specific conditions. The influence of silica dioxide on AgI and ZnI₂ nanocrystals characteristics (phase, size, and thermal stability) were studied using various physicochemical analysis methods. In addition to crystal features, some structural and textural properties of the AgI-ZnI₂-SiO₂ hybrid as an individual agglomerate and its morphology were determined. This showed that nanocrystal features were dependent on synthesis condition. The influence of the nature of the reagent, which is pH-forming, was manifested at the initial stage of the process, and the morphology of the silica dioxide matrix controlled the crystal properties during the post-synthesis phase. It was established that the thermal stability of AgI and ZnI₂ nanocrystals increased due to the protective shielding function of that SiO₂ matrix. Full article

This study presents a sensor-integrated framework for evaluating purslane (Portulaca oleracea L.) stalk flour as a functional ingredient in butter biscuits. A Design of Experiments (DoEs) approach was applied using multisensor probes (electrical conductivity, pH, TDS, ORP) and digital imaging sensors (visible reflectance spectra) for real-time, non-destructive quality assessment. Multivariate analysis with Repeated Relief Feature Selection (RReliefF) and Principal Component Analysis (PCA) reduced 54 initial measurements to 19 informative features, with the first two principal components explaining over 96% of the variance related to flour concentration. Regression modeling combined with linear programming identified an optimal substitution level of 9.62%. Biscuits at this level showed improved texture, enhanced elemental composition (Ca, Mg, Fe, Zn), stable color, and maintained sensory acceptability. The methodology demonstrates a reliable, low-cost sensing and chemometric approach for data-driven, non-destructive quality monitoring and product optimization in food manufacturing. Full article

In the realm of medical image processing, the segmentation of dermatological lesions is a pivotal technique for the early detection of skin cancer. However, existing methods for segmenting images of skin lesions often encounter limitations when dealing with intricate boundaries and diverse lesion shapes. To address these challenges, we propose VMPANet, designed to accurately localize critical targets and capture edge structures. VMPANet employs an inverted pyramid convolution to extract multi-scale features while utilizing the visual Mamba module to capture long-range dependencies among image features. Additionally, we leverage previously extracted masks as cues to facilitate efficient feature propagation. Furthermore, VMPANet integrates parallel depthwise separable convolutions to enhance feature extraction and introduces innovative mechanisms for edge enhancement, spatial attention, and channel attention to adaptively extract edge information and complex spatial relationships. Notably, VMPANet refines a novel cross-attention mechanism, which effectively facilitates the interaction between deep semantic cues and shallow texture details, thereby generating comprehensive feature representations while reducing computational load and redundancy. We conducted comparative and ablation experiments on two public skin lesion datasets (ISIC2017 and ISIC2018). The results demonstrate that VMPANet outperforms existing mainstream methods. On the ISIC2017 dataset, its mIoU and DSC metrics are 1.38% and 0.83% higher than those of VM-Unet respectively; on the ISIC2018 dataset, these metrics are 1.10% and 0.67% higher than those of EMCAD, respectively. Moreover, VMPANet boasts a parameter count of only 0.383 M and a computational load of 1.159 GFLOPs. Full article

Speaker diarization (SD), which aims to address the “who spoke when” problem, is a key technology in speech processing. Although end-to-end neural speaker diarization methods have simplified the traditional multi-stage pipeline, their capability to extract discriminative speaker-specific features remains constrained, particularly in overlapping speech segments. To address this limitation, we propose EEND-ECB-CGA, an enhanced neural network built upon the EEND-VC framework. Our approach introduces a texture-aware fusion module that integrates an Edge-oriented Convolution Block (ECB) with Content-Guided Attention (CGA). The ECB extracts complementary texture and edge features from spectrograms, capturing speaker-specific structural patterns that are often overlooked by energy-based features, thereby improving the detection of speaker change points. The CGA module then dynamically weights the texture-enhanced features based on their importance, emphasizing speaker-dominant regions while suppressing noise and overlap interference. Evaluations on the LibriSpeech_mini and LibriSpeech datasets demonstrate that our EEND-ECB-CGA method significantly reduces the diarization error rate (DER) compared to the baseline. Furthermore, it outperforms several mainstream end-to-end clustering-based approaches. These results validate the robustness of our method in complex, multi-speaker environments, particularly in challenging scenarios with overlapping speech. Full article

To address challenges in small object detection within drone aerial imagery—such as sparse feature information, intense background interference, and drastic scale variations—this paper proposes YOLO-SR, a lightweight detection algorithm based on attention enhancement and feature reuse mechanisms. First, we designed the lightweight feature extraction module C2f-SA, which incorporates Shuffle Attention. By integrating channel shuffling and grouped spatial attention mechanisms, this module dynamically enhances edge and texture feature responses for small objects, effectively improving the discriminative power of shallow-level features. Second, the Spatial Pyramid Pooling Attention (SPPC) module captures multi-scale contextual information through spatial pyramid pooling. Combined with dual-path (channel and spatial) attention mechanisms, it optimizes feature representation while significantly suppressing complex background interference. Finally, the detection head employs a decoupled architecture separating classification and regression tasks, supplemented by a dynamic loss weighting strategy to mitigate small object localization inaccuracies. Experimental results on the RGBT-Tiny dataset demonstrate that compared to the baseline model YOLOv5s, our algorithm achieves a 5.3% improvement in precision, a 13.1% increase in recall, and respective gains of 11.5% and 22.3% in mAP_0.5 and mAP_0.75, simultaneously reducing the number of parameters by 42.9% (from 7.0 × 10⁶ to 4.0 × 10⁶) and computational cost by 37.2% (from 60.0 GFLOPs to 37.7 GFLOPs). The comprehensive improvement across multiple metrics validates the superiority of the proposed algorithm in both accuracy and efficiency. Full article

Methods for the one-stage superhydrophobic silica synthesis with high textural characteristics and a contact angle of up to 163° as a promising functional additive for struvite-based fire extinguishing powder compositions with complex gas-generating effects have been presented. In this work the sequence of components’ introduction into the reaction medium and the water amount influence during the one-stage synthesis of hydrophobic silicon dioxide on its textural, structural, morphological features and water-repellent properties were investigated. Rheological studies and assessment of the hydrophobic properties of fire extinguishing compositions, obtained with the synthesized silicon dioxide particles, allowed determining the optimal characteristics of a functional additive for compositions with struvite. The developed functional additive made it possible to implement the use of crystalline hydrates (struvite) in a fire extinguishing composition. Its inhibitory effect on flames is no less than two times greater than for widely used ammonium phosphates. Full article

To address the issues of uneven population initialization, insufficient individual information interaction, and passive boundary handling in the standard Golden Jackal Optimization (GJO) algorithm, while improving the accuracy and efficiency of multilevel thresholding in artistic image segmentation, this paper proposes an improved Golden Jackal Optimization algorithm (MGJO) and applies it to this task. MGJO introduces a high-quality point set for population initialization, ensuring a more uniform distribution of initial individuals in the search space and better adaptation to the complex grayscale characteristics of artistic images. A dual crossover strategy, integrating horizontal and vertical information exchange, is designed to enhance individual information sharing and fine-grained dimensional search, catering to the segmentation needs of artistic image textures and color layers. Furthermore, a global-optimum-based boundary handling mechanism is constructed to prevent information loss when boundaries are exceeded, thereby preserving the boundary details of artistic images. The performance of MGJO was evaluated on the CEC2017 (dim = 30, 100) and CEC2022 (dim = 10, 20) benchmark suites against seven algorithms, including GWO and IWOA. Population diversity analysis, exploration–exploitation balance assessment, Wilcoxon rank-sum tests, and Friedman mean-rank tests all demonstrate that MGJO significantly outperforms the comparison algorithms in optimization accuracy, stability, and statistical reliability. In multilevel thresholding for artistic image segmentation, using Otsu’s between-class variance as the objective function, MGJO achieves higher fitness values (approaching Otsu’s optimal values) across various artistic images with complex textures and colors, as well as benchmark images such as Baboon, Camera, and Lena, in 4-, 6-, 8-, and 10-level thresholding tasks. The resulting segmented images exhibit superior peak signal-to-noise ratio (PSNR), structural similarity (SSIM), and feature similarity (FSIM) compared to other algorithms, more precisely preserving brushstroke details and color layers. Friedman average rankings consistently place MGJO in the lead. These experimental results indicate that MGJO effectively overcomes the performance limitations of the standard GJO, demonstrating excellent performance in both numerical optimization and multilevel thresholding artistic image segmentation. It provides an efficient solution for high-dimensional complex optimization problems and practical demands in artistic image processing. Full article

Realizing real-time dense 3D reconstruction on resource-limited mobile platforms remains a significant challenge, particularly in low-texture environments that demand robust multi-frame fusion to resolve matching ambiguities. However, the inherent tight coupling of pose estimation and mapping in traditional monolithic SLAM architectures imposes a severe restriction on integrating high-complexity fusion algorithms without compromising tracking stability. To overcome these limitations, this paper proposes MDR–SLAM, a modular and fully decoupled stereo framework. The system features a novel keyframe-driven temporal filter that synergizes efficient ELAS stereo matching with Kalman filtering to effectively accumulate geometric constraints, thereby enhancing reconstruction density in textureless areas. Furthermore, a confidence-based fusion backend is employed to incrementally maintain global map consistency and filter outliers. Quantitative evaluation on the NUFR-M3F indoor dataset demonstrates the effectiveness of the proposed method: compared to the standard single-frame baseline, MDR–SLAM reduces map RMSE by 83.3% (to 0.012 m) and global trajectory drift by 55.6%, while significantly improving map completeness. The system operates entirely on CPU resources with a stable 4.7 Hz mapping frequency, verifying its suitability for embedded mobile robotics. Full article

Grain yield is one of the most critical indicators for evaluating the performance of wheat breeding. However, the assessment process, from early-stage breeding lines to officially registered varieties that have passed the DUS (Distinctness, Uniformity, and Stability) test, is often time-consuming and labor-intensive. Multispectral remote sensing based on unmanned aerial vehicles (UAVs) has demonstrated significant potential in crop phenotyping and yield estimation due to its high throughput, non-destructive nature, and ability to rapidly collect large-scale, multi-temporal data. In this study, multi-temporal UAV-based multispectral imagery, RGB images, and canopy height data were collected throughout the entire wheat growth stage (2023–2024) in Xuzhou, Jiangsu Province, China, to characterize the dynamic growth patterns of both breeding lines and registered cultivars. Vegetation indices (VIs), texture parameters (Tes), and a time-series crop height model (CHM), including the logistic-derived growth rate (GR) and the projected area (PA), were extracted to construct a comprehensive multi-source feature set. Four machine learning algorithms, namely a random forest (RF), support vector machine regression (SVR), extreme gradient boosting (XGBoost), and partial least squares regression (PLSR), were employed to model and estimate yield. The results demonstrated that spectral, texture, and canopy height features derived from multi-temporal UAV data effectively captured phenotypic differences among wheat types and contributed to yield estimation. Features obtained from later growth stages generally led to higher estimation accuracy. The integration of vegetation indices and texture features outperformed models using single-feature types. Furthermore, the integration of time-series features and feature selection further improved predictive accuracy, with XGBoost incorporating VIs, Tes, GR, and PA yielding the best performance (R² = 0.714, RMSE = 0.516 t/ha, rRMSE = 5.96%). Overall, the proposed multi-source modeling framework offers a practical and efficient solution for yield estimation in early-stage wheat breeding and can support breeders and growers by enabling earlier, more accurate selection and management decisions in real-world production environments. Full article