Search Results (447)

Real-time object detection in Unmanned Aerial Vehicle (UAV) imagery presents unique challenges, primarily characterized by extreme scale variations and intense background clutter. Existing detectors often suffer from spectral homogenization in which the critical high-frequency details of minute targets are washed out by dominant background signals during feature downsampling. To address this, we propose Eagle-YOLO, a dynamic feature aggregation framework designed to master these complexities without compromising inference speed. We introduce three core innovations: (1) the Hierarchical Granularity Block (HG-Block), which employs a residual granularity injection pathway to function as a detail anchor for tiny objects while simultaneously accumulating semantics for large structures; (2) the Cross-Stage Context Modulation (CSCM) mechanism, which leverages a global context query to filter background redundancy and recalibrate features across network stages; and (3) the Scale-Adaptive Heterogeneous Convolution (SAHC) strategy, which dynamically aligns receptive fields with the inherent scale distribution of aerial data. Extensive experiments on the DUT Anti-UAV dataset demonstrate that Eagle-YOLO achieves a remarkable balance between accuracy and latency. Specifically, our lightweight Eagle-YOLO-T variant achieves 74.62% AP, surpassing the robust baseline RTMDet-T by 1.67% while maintaining a real-time inference speed of 141 FPS on an NVIDIA RTX 4090 GPU. Furthermore, on the challenging Anti-UAV dataset, our Eagle-YOLOv8-M variant reaches an impressive 94.38% ${\mathrm{AP}}_{50}^{\mathrm{val}}$, outperforming the standard YOLOv8-M by 2.83% and proving its efficacy for edge-deployed aerial surveillance applications. Full article

Surface defects in hydraulic concrete structures exhibit extreme topological heterogeneity. and are frequently obscured by unstructured environmental noise. Conventional detection models, constrained by fixed-grid convolutions, often fail to effectively capture these irregular geometries or suppress background artifacts. To address these challenges, this study proposes the Adaptive Local–Global Synergistic Perception Network (ALGSP-Net). First, to overcome geometric constraints, the Defect-aware Receptive Field Aggregation and Adaptive Dynamic Receptive Field modules are introduced. Instead of rigid sampling, this design adaptively modulates the receptive field to align with defect morphologies, ensuring the precise encapsulation of slender cracks and interlaced spalling. Second, a dual-stream gating fusion strategy is employed to mitigate semantic ambiguity. This mechanism leverages global context to calibrate local feature responses, effectively filtering background interference while enhancing cross-scale alignment. Experimental results on the self-constructed SDD-HCS dataset demonstrate that the method achieves an average Precision of 77.46% and an mAP50 of 72.78% across six defect categories. Comparative analysis confirms that ALGSP-Net outperforms state-of-the-art benchmarks in both accuracy and robustness, providing a reliable solution for the intelligent maintenance of hydraulic infrastructure. Full article

Mining conveyor belts are critical components in bulk material transportation, but their operational safety is frequently threatened by diverse damages such as blocks, cracks, foreign objects, and holes. Existing detection methods, including traditional computer vision and conventional neural networks, struggle to balance accuracy and efficiency in harsh mining environments—marked by high levels of dust, uneven lighting, and extreme scale variability (5–300 pixels). Our study proposes WTConv-YOLO, an improved model based on YOLOv11, integrating two core modules: (1) wavelet transform convolution (WTConv), which achieves a logarithmically expanding receptive field with linearly growing parameters, allowing for the concurrent capture of high-frequency local details and low-frequency global context; (2) Scale-based Dynamic Loss (SD Loss), which dynamically adjusts bounding box similarity and localization loss weights according to target scale, mitigating IoU fluctuation interference and enhancing small-target detection stability. Experiments on the Mining Industrial Conveyor Belt Dataset show that WTConv-YOLOv11 achieves a mean Average Precision (mAP@0.5) of 73.8%—a 3.5% improvement over the baseline YOLOv11. A Python-based software system is developed for end-to-end detection. This work provides a practical solution for reliable conveyor belt damage detection in mining scenarios. Full article

Remote sensing image segmentation requires balancing global context and local detail across multi-scale objects. However, convolutional neural network (CNN)-based methods struggle to model long-range dependencies, while transformer-based approaches suffer from quadratic complexity and become inefficient for high-resolution remote sensing scenarios. In addition, the semantic gap between deep and shallow features can cause misalignment during cross-layer aggregation, and information loss in upsampling tends to break thin continuous structures, such as roads and roof edges, introducing pronounced structural noise. To address these issues, we propose lightweight Lite-BSSNet (Blueprint-Guided State Space Network). First, a Structural Blueprint Generator (SBG) converts high-level semantics into an edge-enhanced structural blueprint that provides a topological prior. Then, a Visual State Space Bridge (VSS-Bridge) aligns multi-level features and projects axially aggregated features into a linear-complexity visual state space, smoothing high-gradient edge signals for sequential scanning. Finally, a Structural Repair Block (SRB) enlarges the effective receptive field via dilated convolutions and uses spatial/channel gating to suppress upsampling artifacts and reconnect thin structures. Experiments on the ISPRS Vaihingen and Potsdam datasets show that Lite-BSSNet achieves the highest segmentation accuracy among the compared lightweight models, with mIoU of 83.9% and 86.7%, respectively, while requiring only 45.4 GFLOPs, thus achieving a favorable trade-off between accuracy and efficiency. Full article

Accurate segmentation of ischemic stroke lesions from multimodal magnetic resonance imaging (MRI) is fundamental for quantitative assessment, treatment planning, and outcome prediction; yet, it remains challenging due to highly heterogeneous lesion morphology, low lesion–background contrast, and substantial variability across scanners and protocols. This work introduces Tri-UNetX-2D, a large-kernel and scale-aware 2D convolutional network with explicit boundary refinement for automated ischemic stroke lesion segmentation from DWI, ADC, and FLAIR MRI. The architecture is built on a compact U-shaped encoder–decoder backbone and integrates three key components: first, a Large-Kernel Inception (LKI) module that employs factorized depthwise separable convolutions and dilation to emulate very large receptive fields, enabling efficient long-range context modeling; second, a Scale-Aware Fusion (SAF) unit that learns adaptive weights to fuse encoder and decoder features, dynamically balancing coarse semantic context and fine structural detail; and third, a Boundary Refinement Head (BRH) that provides explicit contour supervision to sharpen lesion borders and reduce boundary error. Squeeze-and-Excitation (SE) attention is embedded within LKI and decoder stages to recalibrate channel responses and emphasize modality-relevant cues, such as DWI-dominant acute core and FLAIR-dominant subacute changes. On the ISLES 2022 multi-center benchmark, Tri-UNetX-2D improves Dice Similarity Coefficient from 0.78 to 0.86, reduces the 95th-percentile Hausdorff distance from 12.4 mm to 8.3 mm, and increases the lesion-wise F1-score from 0.71 to 0.81 compared with a plain 2D U-Net trained under identical conditions. These results demonstrate that the proposed framework achieves competitive performance with substantially lower complexity than typical 3D or ensemble-based models, highlighting its potential for scalable, clinically deployable stroke lesion segmentation. Full article

Precise identification of weeds at the maize seedling stage is pivotal for implementing Site-Specific Weed Management and minimizing herbicide environmental pollution. However, the performance of existing lightweight detectors is severely bottlenecked by unstructured field environments, characterized by the “green-on-green” spectral similarity between crops and weeds, diminutive seedling targets, and complex mutual occlusion of leaves. To address these challenges, this study proposes SLD-YOLO11, a topology-reconstructed lightweight detection model tailored for complex field environments. First, to mitigate the feature loss of tiny targets, a Lossless Downsampling Topology based on Space-to-Depth Convolution (SPD-Conv) is constructed, transforming spatial information into depth channels to preserve fine-grained features. Second, a Decomposed Large Kernel Attention (D-LKA) mechanism is designed to mimic the wide receptive field of human vision. By modeling long-range spatial dependencies with decomposed large-kernel attention, it enhances discrimination under severe occlusion by leveraging global structural context. Third, the DySample operator is introduced to replace static interpolation, enabling content-aware feature flow reconstruction. Experimental results demonstrate that SLD-YOLO11 achieves an mAP@0.5 of 97.4% on a self-collected maize field dataset, significantly outperforming YOLOv8n, YOLOv10n, YOLOv11n, and mainstream lightweight variants. Notably, the model achieves Zero Inter-class Misclassification between maize and weeds, establishing high safety standards for weeding operations. To further bridge the gap between visual perception and precision operations, a Visual Weed-Crop Competition Index (VWCI) is innovatively proposed. By integrating detection bounding boxes with species-specific morphological correction coefficients, the VWCI quantifies field weed pressure with low cost and high throughput. Regression analysis reveals a high consistency (R² = 0.70) between the automated VWCI and manual ground-truth coverage. This study not only provides a robust detector but also offers a reliable decision-making basis for real-time variable-rate spraying by intelligent weeding robots. Full article

The experiences and repercussions of the cross-border migration process on family life have not yet been synthesized. This study aimed to synthesize the best available qualitative findings on this theme. A systematic review of qualitative evidence with meta-synthesis was conducted. Articles were identified according to predefined extraction criteria in the first half of 2025, across seven databases: Web of Science, MEDLINE/PubMed, PsycINFO, LILACS, CINAHL, SCOPUS, and Social Science Citation Index. Two researchers independently screened and appraised the reports, assessing methodological quality and systematically recording and analyzing relevant information. A protocol was registered in PROSPERO (ID: CRD42024505655). Fifty studies were included, and three main themes emerged: (a) living in multiple possible contexts, where space and relationships influence family functionality, including reduced family time due to long working hours, substance use, fear of losing cultural roots, new financial responsibilities, and the desire to return to the country of origin; (b) challenges and repercussions on family life after migration, such as increased family conflicts, mental health problems, separation, and loss of ties; (c) strategies for maintaining family functioning, including role adjustment, strengthening of family ties, and support through cultural and religious practices. Families undergoing migration face multiple challenges in their new environments, revealing the complexity of adapting to diverse cultural and social contexts. These findings highlight the need to address the emotional and social demands of migrant families to improve well-being and integration. Understanding these dynamics allows healthcare professionals to design culturally sensitive interventions that promote reception and inclusion. Full article

Accurate segmentation of fruit tree leaf diseases is critical for yield protection and precision crop management, yet it is challenging due to complex field conditions, irregular leaf morphology, and diverse lesion patterns. To address these issues, Disease-Seg, a lightweight real-time segmentation framework, is proposed. It integrates CNN and Transformer with a parallel fusion architecture to capture local texture and global semantic context. The Extended Feature Module (EFM) enlarges the receptive field while retaining fine details. A Deep Multi-scale Attention mechanism (DM-Attention) allocates channel weights across scales to reduce redundancy, and a Feature-weighted Fusion Module (FWFM) optimizes integration of heterogeneous feature maps, enhancing multi-scale representation. Experiments show that Disease-Seg achieves 90.32% mIoU and 99.52% accuracy, outperforming representative CNN, Transformer, and hybrid-based methods. Compared with HRNetV2, it improves mIoU by 6.87% and FPS by 31, while using only 4.78 M parameters. It maintains 69 FPS on 512 × 512 crops and requires approximately 49 ms per image on edge devices, demonstrating strong deployment feasibility. On two grape leaf diseases from the PlantVillage dataset, it achieves 91.19% mIoU, confirming robust generalization. These results indicate that Disease-Seg provides an accurate, efficient, and practical solution for fruit leaf disease segmentation, enabling real-time monitoring and smart agriculture applications. Full article

This article examines the visual reception of the woman of Endor (1 Sam 28) and her gradual integration into the Western imaginary of the witch. In the first section, it offers a concise overview of the formation of witchcraft in late medieval and early modern visual culture, when iconographic and discursive registers contributed to the consolidation of a demonological and persecutory repertoire associated with the female body. Against this background, the study analyzes how the figure of Endor came to be interpreted and represented through increasingly negative categories—eventually becoming a conventionalized motif in the history of art—despite the fact that the biblical narrative originally presents her as a ritual mediator whose role in Saul’s episode is not constructed as a paradigmatic case of “witchcraft” in a strict sense. Drawing on a methodology of visual exegesis that brings together cultural biblical studies, art history, and gender studies, this article examines a range of artworks depicting the episode in order to show how visual culture negotiates the boundary between the legitimate and the forbidden, and how the later demonization of Endor reveals persistent tensions between orthodoxy and heterodoxy across different historical contexts. Full article

Monocular depth estimation is a fundamental task with broad applications in autonomous driving and augmented reality. While recent lightweight methods achieve impressive performance, they often neglect the interaction of mid-order semantic features, which are crucial for capturing object structures and spatial relationships that directly impact depth accuracy. To address this limitation, we propose MogaDepth, a lightweight yet expressive architecture. It introduces a novel Continuous Multi-Order Gated Aggregation (CMOGA) module that explicitly enhances mid-level feature representations through multi-order receptive fields. In addition, we present MambaSync, a global–local interaction unit that enables efficient feature communication across different contexts. Extensive experiments demonstrate that MogaDepth achieves highly competitive or superior performance on KITTI, improving key error metrics while maintaining comparable model size. On the Make3D benchmark, it consistently outperforms existing methods, showing strong robustness to domain shifts and challenging scenarios such as low-texture regions. Moreover, MogaDepth achieves an improved trade-off between accuracy and efficiency, running up to 13% faster on edge devices without compromising performance. These results establish MogaDepth as an effective and efficient solution for real-world monocular depth estimation. Full article

Landslide hazard assessment increasingly demands the joint analysis of heterogeneous remote sensing data; however, automating this process remains difficult due to the pronounced resolution and texture discrepancies existing between satellite and aerial sensors. To address these limitations, this study proposes a robust segmentation framework capable of extracting sensor-robust representations. The framework leverages a DINOv3 transformer encoder and exploits representations from multiple transformer layers to capture complementary visual information, ranging from fine-grained surface textures to global semantic contexts, overcoming the receptive field constraints of conventional CNNs. Experiments on the Longxi satellite dataset achieve a Dice coefficient of 0.96 and an IoU of 0.938, and experiments on the Longxi UAV dataset achieve a Dice coefficient of 0.965 and an IoU of 0.941. These results show consistent segmentation performance on both the Longxi satellite and UAV datasets, despite differences in spatial resolution and surface appearance between acquisition platforms. Full article

Fusion cuisine blends ingredients, cooking techniques, and flavours from different cultures, yet little is known about how it is perceived within the context of gastrodiplomacy. This study explores perceptions of fusion cuisine at a multicultural gastrodiplomacy event held in Kosovo, where the participants first sampled Turkish–Kosovar fusion dishes during tasting sessions and subsequently completed an online questionnaire designed to assess their experience. In this event, participants attended structured tasting activities in Prizren and Pristina, where they sampled dishes combining elements of both culinary traditions, and then completed an online structured questionnaire consisting of 5-point Likert-type items evaluating their fusion cuisine preferences. The study was conducted in Kosovo as part of a unilateral gastrodiplomatic initiative. A total of 451 participants responded to an online questionnaire, which included fusion cuisine preference scores and metaphorical descriptions of their culinary experiences. A key contextual characteristic of this study is that data were collected exclusively during a fusion cuisine event held in Kosovo, with participation from a multinational audience who attended the event. Therefore, the sample reflects diverse cultural backgrounds within a single-location setting. The results indicate that younger, highly educated, and higher-income participants exhibited significantly greater openness to culinary diversity. These findings advance the state of knowledge by demonstrating that public reception of gastrodiplomacy is stratified by socioeconomic factors rather than defined solely by national background. Practically, this implies that effective fusion-based diplomacy requires targeted strategies to bridge demographic gaps and ensure broader social inclusivity, rather than relying on a one-size-fits-all approach. Full article

Automatic Identification System (AIS) signals are critical for maritime traffic monitoring and collision avoidance. In satellite reception environments, signal collisions occur frequently due to large coverage areas and high ship density, severely degrading decoding performance. We propose a dual-branch deep learning architecture that combines precise boundary detection with segment-level classification to address this collision problem. The network employs a multi-scale convolutional backbone that feeds two specialized branches: one detects collision boundaries with sample-level precision, while the other provides semantic context through segment classification. We developed a satellite AIS dataset generation framework that simulates realistic collision scenarios including multiple ships, Doppler effects, and channel impairments. The trained model achieves 96% collision detection accuracy on simulated data. Validation on real satellite recordings demonstrates that our method retains 99.4% of valid position reports compared to direct decoding of the original signal. Controlled experiments show that intelligent collision removal outperforms random segment exclusion by 6.4 percentage points, confirming the effectiveness of our approach. Full article

Whilst there has been substantial attention to care-experienced parents’ needs and experiences in the academic literature internationally, understandings of nascent services, their characteristics and implementation processes are more limited. With an overarching socioecological resilience systems framing and drawing on an innovation perspective, we aim to develop understanding of how to design and develop innovations to enable care-experienced parents’ successing. We conducted a narrative literature review that included 33 sources published internationally between 2017 and 2025. We conducted thematic analysis to identify adversities experienced by and innovations developed for care-experienced parents. We authenticated the themes in a workshop with members of the practice community and developed frameworks to represent the themes. Findings are represented in three sections. First, we consider parental needs, with an overview of adversities experienced by care-experienced parents together with individual protective factors and required service responses, framed by psychological, social and structural domains. Second, drawing upon such understanding, we consider intervention design, with a focus on exemplar innovations and the characteristics that are represented by five service delivery models: therapeutic; social; partnership; advocacy; and co-production. Third, with a need to ensure that service intervention is effective, we examine the process of developing service innovations and consider five dynamic ingredients that enable implementation success: shared leadership; receptivity of context; co-production; learning and adaption; and outcome measurement. Our review contributes new understanding to inform processes of designing and implementing innovations to enable care-experienced parents’ successing. We offer a framework that represents a starting point towards enabling care-experienced parents’ successing that can be applied in policy and practice, although more research is needed. Full article

Detecting vehicles in remote sensing images of construction sites captured by Unmanned Aerial Vehicles (UAVs) faces severe challenges, including extremely small target scales, high inter-class visual similarity, cluttered backgrounds, and highly variable imaging conditions. To address these issues, we propose BSGNet (Biomimetic Sharpening and Global Receptive Field Network)—a novel detection architecture that synergistically fuses biologically inspired visual mechanisms with global receptive field modeling. Inspired by the Sustained Contrast Detection (SCD) mechanism in frog retinal ganglion cells, we design a Perceptual Sharpening Module (PSM). This module combines dual-path contrast enhancement with spatial attention mechanisms to significantly improve sensitivity to the high-frequency edge structures of small targets while effectively suppressing interfering backgrounds. To overcome the inherent limitation of such biomimetic mechanisms—specifically their restricted local receptive fields—we further introduce a Global Heterogeneous Receptive Field Learning Module (GRM). This module employs parallel multi-branch dilated convolutions and local detail enhancement paths to achieve joint modeling of long-range semantic context and fine-grained local features. Extensive experiments on our newly constructed UAV Construction Vehicle (UCV) dataset demonstrate that BSGNet achieves state-of-the-art performance: obtaining 64.9% APs on small targets and 81.2% on the overall mAP@0.5 metric, with an inference latency of only 31.4 milliseconds, outperforming existing mainstream detection frameworks in multiple metrics. Furthermore, the model demonstrates robust generalization performance on public datasets. Full article