Search Results (265)

Multiscale surface characterization is essential for understanding the surface condition of laser powder bed fusion (LPBF)-fabricated metallic structures and regions subjected to different post-processing conditions. This study examines LPBF-fabricated 17-4 PH stainless steel (SS) triply periodic minimal surface (TPMS) prototypes to characterize geometry-dependent manufacturing features and representative flat plate regions associated with aging, shot blasting, hydrophobic coating, and combined blasting-coating conditions. Hirox digital microscopy was used to examine macro- to microscale surface features on the TPMS prototypes and flat plates, while Hirox 3D profiling was used to evaluate the roughness response of the flat plate treatment regions. Scanning electron microscopy (SEM) and energy-dispersive X-ray spectroscopy (EDS) provided higher-resolution surface morphology and elemental composition from selected flat plate regions. Hirox imaging revealed anisotropic surface textures aligned with LPBF scan paths, pore presence, and staircase-like features on curved TPMS surfaces. Three-dimensional profiling identified different geometric mean Rz values among the examined flat plate regions; the blasted-plus-coated regions on the untreated baseline plate, U-BC, and the aged plate, A-BC, had the smallest reported values within their respective plate sets. SEM observations identified LPBF-related features, including balling, pores, particle adhesion, localized surface irregularities, and coating cracks. Localized EDS analysis showed Fe-, Cr-, Ni-, Cu-, and Si-containing elemental profiles in the examined U-0 and A-0 regions, whereas the analyzed U-C region exhibited strong Si and O signals, coating-associated elemental features, and reduced substrate contribution. These results demonstrate the importance of combining surface morphology, profile roughness, coating condition, and localized elemental composition to document geometry-dependent TPMS features and regional surface condition differences across the examined flat plates. Full article

Rail-surface defects are often small, low-contrast, and confused with steel texture or reflections, making it difficult to improve accuracy without increasing model cost. This study proposes ESiV-YOLOv8, a compact detector that assigns complementary modifications to feature selection, box regression, and multiscale fusion in YOLOv8n. EffectiveSE recalibrates deep backbone features, SIoU provides direction-aware regression, and a VoV-GSCSP/GSConv neck reduces redundant computation. Evaluation used a self-built four-class dataset of 4020 images, a near-duplicate-aware train-validation-test split, and matched seven-seed experiments. ESiV-YOLOv8 achieved 97.7% precision, 94.6% recall, 97.2% mAP@0.5, and 68.6% mAP@0.5:0.95. Relative to YOLOv8n, mAP@0.5:0.95 increased by 5.0 percentage points, while parameters and GFLOPs decreased by 17.1% and 12.2%, respectively. On the combined natural-condition subset, ESiV-YOLOv8 achieved 59.6% mAP@0.5:0.95, 5.6 percentage points above the baseline. The annotation audit yielded 98.0% class agreement and a mean box IoU of 0.89. Model-only and end-to-end latency increased by 1.4% and 2.2%, respectively. Overall, ESiV-YOLOv8 improves detection accuracy and reduces model scale with limited latency overhead on the tested backend. Full article

Molluscan shells combine mineralized layers with distinct microstructures that can influence crack propagation. Here, we provide a through-thickness, multiscale characterization of the all-aragonitic shell of the striped venus clam Chamelea gallina and relate its hierarchical textural organization to observed fracture-surface trajectories. Optical microscopy, scanning electron microscopy, energy-dispersive X-ray spectroscopy, and spatially resolved Fourier-transform infrared spectroscopy and X-ray diffraction resolve an outer region comprising (i) convergent and divergent fibrous sublayers separated by a preferred fracture plane, (ii) a porous transition layer with inverse-tulip microstructures, and (iii) a structurally distinct homogeneous inner layer whose crossed-lamellar organization becomes visible after etching. The key advance is the resolution of this outer architecture from nanogranule alignment to curved fibrous layers and a porous transition region, together with the identification of recurring changes in fracture-surface direction at its interfaces. FTIR and XRD reveal through-thickness variations in vibrational-band ratios and crystallographic parameters. As the fracture analysis is based on post-fracture morphology, crack steering and damage localization are discussed as plausible structure-related mechanisms supported by the observed features. The architecture suggests transferable design principles, including graded orientation, interface-guided deflection, and localized porosity, for mechanically robust bioinspired composites and provides a structural framework for future studies of shell evolution and function. Full article

Semantic segmentation of architectural elements in cultural heritage sites lies at the intersection of computer vision and digital preservation. Moroccan historical monuments spanning mosques, madrasas, royal gates (babs), and mausoleums across Fez, Rabat, Marrakech, Meknes, and Tetouan present unique challenges, including extreme texture ambiguity between weathered wall surfaces and background, pronounced multi-scale variation from individual window and door openings to full rooftop surfaces spanning tens of metres, and severe class imbalance. We introduce MonuSegFormer, a heritage-specific hybrid architecture coupling a pretrained Swin-B encoder (ImageNet-22K) with a Multi-Scale Atrous Fusion (MSAF) module and a CBAM-augmented progressive decoder. Evaluated on the Moroccan Monuments Dataset (MMD, 2686 annotated RGB images, 5 semantic classes, 5 cities) on the held-out test set (403 images), MonuSegFormer achieves mIoU = 81.7%, outperforming SegFormer-B5 (76.4%), Mask2Former (78.9%), and DeepLabV3+ (71.3%). A composite CE+Dice+Focal loss with class-balanced weights addresses severe class imbalance, yielding the largest gains on the minority classes Door (+5.3 p.p.) and Window (+5.3 p.p.) over the next-best Mask2Former. All qualitative results are produced by real trained-model inference on held-out test images. Full article

Construction material identification is important for automated construction monitoring, digital twin generation, and building information modelling. Terrestrial laser scanning (TLS) provides dense geometric information together with LiDAR intensity measurements; however, reliable material discrimination remains challenging because intensity is affected by acquisition geometry and surface texture may vary depending on the spatial scale at which it is characterised. This study proposes a TLS-only machine-learning framework combining a reflectance-related intensity–geometry regression descriptor with multiscale geometric roughness features. Plane-residual roughness and normal-variation roughness were calculated using local cube neighbourhoods with side lengths of 0.10, 0.20, and 0.30 m. To avoid ambiguity associated with surface-normal orientation, the normal-variation descriptor was calculated using orientation-invariant angular differences between neighbouring surface normals. The training dataset was balanced using distance-stratified random undersampling, while the held-out test dataset retained its original class distribution. FLAML was used for automated model selection and hyperparameter optimisation using three-fold cross-validation with macro F1-score as the optimisation metric. The final stacking classifier achieved an overall accuracy of 90.44%, balanced accuracy of 87.42%, and macro F1-score of 87.85% on 1,296,822 held-out test points. The held-out test data were acquired from different scanner positions and spatially distinct material regions from those used for training, with no shared point samples; however, both datasets originated from the same general study area, and broader cross-site generalisation therefore requires further independent validation. Most material classes showed strong discrimination, although Carpet remained challenging because of confusion with Asphalt. The results demonstrate the potential of combining TLS-derived radiometric information with multiscale geometric surface descriptors for construction material classification without relying on RGB colour information. Full article

Climate-related and environmental hazards affect cultural heritage sites in markedly different inland, coastal, lacustrine and underwater settings, creating documentation requirements that cannot be addressed by a single sensing approach. This study presents the multi-sensor geometric documentation of eight cultural heritage sites. Unmanned aerial vehicle (UAV) photogrammetry was applied to six inland and coastal sites, while underwater photogrammetry, unmanned surface vehicles (USVs), acoustic sounding and a prototype green-wavelength flash LiDAR were used at three shallow-water sites. The campaigns produced orthomosaics, elevation models, dense point clouds, textured meshes, bathymetric maps and underwater LiDAR point clouds at scales appropriate to the conservation problem of each site. The resulting products document exposed architectural remains, excavation areas, cliffs and unstable slopes, lake-margin changes, submerged masonry, wooden structures and lakebed morphology. Their main contribution is the establishment of spatially explicit, site-specific baselines that provide measurable geometric and visual evidence for condition assessment, future repeat-survey comparisons and the spatial integration of environmental, archaeological and conservation information. The study demonstrates the operational and information complementarity of optical, acoustic and active ranging approaches, which address different documentation scales, environmental constraints and heritage targets, and provide distinct spatial evidence that can serve as potential inputs to subsequent digital twin and decision support applications. Full article

High-entropy alloys (HEAs) and multi-principal-element alloys (MPEAs) provide broad compositional flexibility for regulating phase stability, microstructure, and properties. However, nominal composition and average phase constitution alone are insufficient to describe the actual material state formed during processing and service. This review summarizes the thermodynamic and diffusion-kinetic basis of phase formation and compares five representative fabrication routes, including mechanical alloying, vacuum melting, severe plastic deformation, magnetron sputtering, and additive manufacturing. Particular attention is given to the effects of processing history on grain structure, texture, elemental segregation, defects, phase constitution, and local chemical order. Computational methods and multiscale characterization techniques are also discussed in relation to the identification and interpretation of processing-dependent material states. Current studies indicate that alloys with identical nominal compositions can exhibit different microstructures and properties because of differences in thermal history, strain path, elemental redistribution, defect populations, and post-processing conditions. The review further examines strength and ductility, corrosion resistance, oxidation resistance, irradiation tolerance, and catalytic performance, with emphasis on the evolution of microstructure and surface state under service conditions. These results indicate that reliable evaluation of HEAs and MPEAs requires consideration of processing reproducibility, structural heterogeneity, and long-term stability rather than isolated peak properties. This processing–structure–service perspective provides a basis for more reliable comparison, selection, and engineering assessment of HEAs and MPEAs under application-relevant conditions. Future research should focus on reproducible fabrication, integration of computational prediction with experimental validation, multiscale assessment of structural evolution, long-term service performance, scalable processing, and sustainable alloy design. Full article

Permafrost degradation is intensifying differential settlement, structural deformation, and secondary periglacial hazards along linear infrastructure in cold regions, underscoring the need for monitoring approaches that integrate corridor-scale screening with fine-scale quantification. This study investigated highways, railways, transmission tower foundations, and buried pipelines in the permafrost region of Northeast China using multi-temporal UAV-borne LiDAR point clouds and synchronous visible-light imagery acquired by a DJI Matrice 300 unmanned aerial vehicle equipped with a DJI Zenmuse L1 sensor (DJI, Shenzhen, China). A synergistic optical–LiDAR framework was developed for distress identification and multidimensional quantification. The overall root mean square errors (RMSEs) at flight altitudes of 50 m and 100 m were 3.25 cm and 4.13 cm, respectively. By integrating texture and boundary information from synchronous visible-light imagery, elevation and volumetric metrics from LiDAR-derived digital elevation models (DEMs) and digital surface models (DSMs), and structural attitude parameters extracted from three-dimensional (3D) models, the framework enabled the parametric quantification of pavement cracking, differential shoulder settlement, railway embankment slump, transmission tower inclination, thaw settlement and ponding in pipeline trenches, and secondary icing. Snow-depth retrievals agreed well with field measurements (R² = 0.87, RMSE = 1.32 cm), indicating that UAV-LiDAR can extend monitoring into snow-covered periods. These findings provide a methodological basis for distress detection, screening of hazard-prone sections, and risk-informed operation and maintenance of linear infrastructure in permafrost regions. Full article

Pavement deterioration poses substantial technical-economic burdens and brings notable social-environmental risks: degraded road surfaces increase vehicle maintenance expenditures, raise traffic accident probabilities, lower transportation operational efficiency, and produce extra carbon emissions caused by aggravated fuel consumption. Automatic pavement defect detection is therefore a vital technical prerequisite for timely road maintenance and mitigating those adverse consequences. However, existing methods suffer from two prominent limitations: missed detection of subtle defects under complex scenarios and high model complexity. To tackle the above limitations, this paper proposes CAR-YOLO, a lightweight pavement defect detection algorithm built upon an improved YOLOv11n. First, a convolution-based context-guided module is incorporated to enhance the exploitation of defect contextual information, thus improving the feature representation quality and detection accuracy of subtle defects. Second, the original neck network is replaced with the attention scale sequence fusion framework (ASF), which fuses multi-scale features to boost detection performance for subtle, complex-texture pavement defects. Finally, the lightweight RepViT network is introduced into the backbone network to streamline the model architecture and reduce model complexity while maintaining detection precision. Experimental results demonstrate that the CAR-YOLO model achieves an ${mAP}_{50}$ of 63.8% on the RDD2022 Chinese UAV dataset, approximately 5% higher than the baseline YOLOv11n. The parameter count is reduced from 2.58 M to 1.71 M, corresponding to a compression ratio of 33.6%; the computational load drops from 6.3 G to 4.4 G, a reduction of around 30.2%. While delivering higher detection accuracy, the proposed algorithm effectively reduces the parameter scale and computational overhead. Full article

Accurate defect instance segmentation is essential for automated quality inspection of piezoelectric crystals, where scratches, stains, and other surface defects often show large scale variation, irregular morphology, and weak contrast against complex backgrounds. Existing CNN-based segmentation models capture local texture effectively but are limited in global context modeling, whereas Transformer-based models often require stronger local detail preservation for fine industrial defects. To address these limitations, this paper proposes a hybrid bidirectional bridging CNN–Transformer network, named HBCTNet, for piezoelectric crystal defect instance segmentation. HBCTNet introduces a Convolution-Transformer Bidirectional Bridging Block (CTB) that enables lightweight two-way interaction between convolutional local features and Transformer global representations. A Frequency and Spatial Convolution (FSC) module is designed to enhance defect-related details in both spatial and frequency domains, and a Multi-Scale Feature Pyramid Network (MS-FPN) fuses features across scales. On the reported image partition of the self-constructed dataset, HBCTNet-S obtains 92.3% box AP and 89.9% mask AP with 12.67 M parameters and 48.1 G FLOPs. Its mask AP is 2.1 percentage points higher than YOLO11-SEG and 2.4 percentage points higher than YOLOv9-SEG under the reported comparison. These results support the effectiveness of the architecture within the evaluated dataset; inference speed and cross-domain generalization remain to be assessed. Full article

Recent image diffusion models have enabled automatic 3D object creation from text or image guidance, but their 2D generative priors often bake illumination and shadow into textures, making relighting and physically based rendering (PBR) difficult. To address this issue, we propose MaterialSeg3D, a framework that predicts surface materials for 3D assets by leveraging 2D material semantics. Given a mesh and its albedo UV map, MaterialSeg3D renders multi-view images, performs material segmentation using a 2D prior model, projects the predictions back to UV space, and fuses them through weighted voting and region unification to obtain coherent material maps. To train the prior model, we construct MIO++, a large-scale single-object material segmentation dataset containing 115,542 images, 12 object themes, and 30 fine-grained material categories, substantially extending the previous MIO dataset. Each MIO++ material category is associated with an independent pair of roughness and metallic values for PBR assignment. We further observe that poor topology in AI-generated assets can degrade PBR quality even when plausible materials are assigned, and introduce a plane-simplification strategy as an auxiliary preprocessing step for such meshes. Experiments show that MIO++ improves material segmentation and that the resulting material maps support more consistent relightable renderings for both human-crafted and AI-generated 3D assets. Full article

Building façade damage segmentation remains challenging because cracks, peeling, hollow-like areas, stains, and erosion often exhibit weak contrast, irregular boundaries, discontinuous structures, and substantial scale variations under complex surface textures and illumination conditions. To address these challenges, this study proposes MMDF-Net, a multimodal instance segmentation network for paired RGB–IR façade damage images. The network adopts dual RGB and infrared branches and performs multi-scale cross-modal interaction across P2–P5 levels. A Frequency-Decoupled Cross-Modal Bridge is designed to separately model low-frequency material responses and high-frequency local damage details, thereby enabling gated bidirectional exchange between the two modalities. A Crack Topology-Aware Encoder further enhances the continuity of thin, curved, branched, and locally discontinuous cracks through directional strip convolution and bending-aware modeling. In addition, a Multi-Scale Damage Decoding Pyramid integrates high-resolution boundary details, mid-level structural cues, and high-level semantic context to support stable mask prediction across different damage scales. Experiments were conducted on a custom-built RGB–IR building façade damage dataset containing 1500 paired image samples across five damage categories. MMDF-Net achieved 82.1% mask precision, 78.6% mask recall, 72.3% mask mAP50, and 65.6% mask mAP50–95, with 7.8 M parameters and 23.5 GFLOPs, outperforming representative RGB and RGB–IR segmentation baselines. These results indicate that task-oriented multimodal feature organization can improve façade damage segmentation without relying on excessive model expansion. Full article

To address the challenges of unreliable feature matching, high mismatch rates, and limited reconstruction accuracy in binocular stereo vision applied to asphalt pavement with inherent weak texture features, this paper proposes a speckle-assisted binocular 3D reconstruction method based on a multi-scale adaptive feature fusion algorithm. Infrared speckle patterns are actively projected to enrich the pavement surface features, and a multi-scale matching framework is developed by integrating Laplacian pyramid representations, feature-driven adaptive regularization, and Softmax-based nonlinear fusion. This design can achieve stable and accurate disparity estimation, even in weak texture regions, and produce high-quality 3D point clouds that faithfully represent both macro-scale undulations and micro-scale texture details. Ablation experiments validate the effectiveness of the proposed modules, showing that the relative errors of the arithmetic mean height (S_a) and root-mean-square height (S_q) are reduced to below 2.3%. When aligned with 3D scanner data using the iterative closest point (ICP) algorithm, the reconstructed point clouds achieve sub-millimeter mean error and an overlap rate exceeding 97%. The results indicate that the proposed method offers a reliable technical solution for efficient texture-depth analysis and practical pavement condition assessment. Full article

Complex surfaces exhibit hierarchical morphological organizations that cannot be fully described by conventional roughness parameters alone. In this study, a fractal–statistical framework is proposed to elucidate the substrate-controlled morphological evolution of 2,6-diphenyl anthracene (DPA) thin films deposited on chemically modified dielectric substrates, including hexamethyldisilazane (HMDS), octyltrimethoxysilane (OTMS), octadecyltrichlorosilane (OTS), and bare silicon dioxide (SiO₂). A multidimensional morphological descriptor vector (M_DPA) is introduced by integrating ISO 25178 areal surface parameters (H_ISO), fractal dimension (D_f), texture direction parameters (T_d), power spectral density (PSD), and scale-sensitive fractal analysis (SSFA) descriptors to quantify amplitude-based, spatial-frequency, and scale-dependent morphological information. Atomic force microscopy (AFM) topographies of 5 nm and 50 nm thick films were analyzed using complementary approaches, including ISO 25178 areal surface parameters, texture direction analysis, peak statistics, morphological envelope fractal analysis, two-dimensional Fourier analysis, power spectral density (PSD), and scale-sensitive fractal analysis (SSFA). The results demonstrate that substrate chemistry governs not only the amplitude of surface roughness but also the lateral organization, spatial frequency distribution, and scale-dependent fractal complexity of DPA morphologies. The fractal dimension analysis revealed substrate-dependent variations in surface complexity, with values ranging from 2.11 to 2.45 for 5 nm films and from 2.19 to 2.52 for 50 nm films. PSD analysis identified distinct substrate-induced modifications in spectral organization, while SSFA revealed significant changes in smooth–rough crossover scales, maximum complexity scales, and fractal surface complexity during film growth. In particular, OTMS promoted the strongest hierarchical organization for thicker films, exhibiting the highest scale-sensitive fractal complexity, whereas OTS generated highly developed but less hierarchically correlated rough structures. The integrated fractal–spectral methodology establishes quantitative relationships between substrate functionalization and multiscale surface evolution, demonstrating that morphological complexity cannot be described solely by conventional height parameters. This framework provides a robust approach for characterizing hierarchical thin-film architectures and can be extended to other organic semiconductor systems where substrate-driven morphological control is critical. Full article

Facial surface temperature provides useful non-contact information for monitoring dairy-cow health, welfare, heat stress, and reproductive status. Infrared thermography can capture thermal information from facial regions such as the eyes, muzzle, nostrils, and ears; however, infrared images often contain weak texture and indistinct anatomical boundaries, which can hinder reliable cow-head localization during mobile robotic inspection. Visible-light images provide richer structural information but do not contain temperature data. This study developed YOLO11-AFE, a lightweight visible–thermal cow-head detection and heterogeneous-camera association method for quadruped inspection robots. The detector incorporates ADown for lightweight downsampling, C3k2_FE for adaptive feature enhancement, and SPPF_ECA for channel-aware multi-scale representation. An improved Hungarian matching algorithm was subsequently used to establish one-to-one correspondences between cow-head detections in synchronized visible-light and infrared pseudo-colour images. Across three independent runs, YOLO11-AFE achieved precision, mAP@0.5, and mAP@0.5:0.95 values of 97.59 ± 0.20%, 96.37 ± 0.24%, and 70.76 ± 0.51%, respectively, on the visible-light test subset, and 96.11 ± 0.24%, 99.07 ± 0.10%, and 91.50 ± 0.40%, respectively, on the infrared subset. The model required 2.14 million parameters and 5.27 GFLOPs, representing reductions of 17.37% and 18.17%, respectively, relative to YOLO11n. The association method achieved an overall accuracy of 98.11% across 371 ground-truth cow-head pairs in the combined validation and test evaluation. TensorRT FP16 deployment on the Jetson Orin NX achieved 36.71 FPS for the complete core processing pipeline. These results demonstrate that YOLO11-AFE provides an accurate and computationally efficient perception front end for future non-contact facial-temperature monitoring using mobile inspection robots. Full article