Search Results (313)

Rail light-strip morphology reflects the wheel-rail contact condition. Reliable automatic analysis remains difficult. The strip is narrow and has weak boundaries, while specular reflection, rail-head texture and trackside background interfere with color inspection images. This study proposes a segmentation-guided geometric method for rail light-strip abnormality analysis. An improved SegFormer jointly segments the background, rail-head and light-strip regions. A boundary detail enhancement module refines weak rail-head and light-strip contours. Focal Loss emphasizes minority and hard boundary pixels. The rail-head mask provides the geometric reference for extracting the light-strip centerline, eccentricity, width sequence and connected-component morphology. The predicted masks are ordered using the corrected mileage record. Every 1000 original-resolution rows then form a consecutive 1 m detection unit. When a geometric rule is triggered, the method reports that unit’s 1 m mileage interval together with its eccentricity, width-change or local-integrity measurement. The model achieves 95.67% mean Intersection over Union (mIoU) on 3520 annotated images. It detects 845 of 876 positive units, with 96.46% recall, 89.23% precision and 92.70% F1-score. The resulting records identify abnormal 1 m mileage intervals and report the corresponding eccentricity, width-change, or local-integrity measurements for targeted manual review. Full article

The utilisation of microwave radiances is crucial for enhancing the precision of weather forecasts. Despite existing uncertainties over land and ice-covered surfaces, recent advances have enhanced their use. This study examines the impact of assuming either Lambertian or specular surface reflection on the simulation of brightness temperatures for surface-sensitive, clear-sky AMSU-A microwave radiances across land and snow-covered areas. It represents the preliminary work before running a full assimilation and forecast impact study. Using the high-resolution HARMONIE-AROME regional modelling system, experiments were conducted to retrieve and analyse the retrieved emissivity in different conditions/seasons. The emissivity was also used as input to the radiative transfer model to simulate brightness temperatures of surface-sensitive sounding channels. The results show that the Lambertian assumption produces higher variability in dynamic surface emissivity, while the specular approach yields smaller and more consistent deviations. During winter, specular reflection shows higher first-guess departures (e.g., observations minus simulations) for surface-sensitive sounding observations, whereas in summer it performs better over land surfaces. Over snow-covered regions, the use of the Lambertian reflection to simulate the brightness temperature gives smaller mean errors for AMSU-A channels 4 (52.8 GHz) and 5 (53.59 GHz). These findings encourage further investigation into implementing a parameter that accounts for the Lambertian component of surface reflection when simulating brightness temperature in high-resolution limited-area models. Additionally, these findings provide practical guidance for configuring complex Nordic surface regional models and for future Arctic Weather Satellite microwave radiance assimilation. Full article

Dental caries remains one of the most prevalent oral health problems worldwide, yet automated detection in intraoral photographs is challenging because of variable lighting, specular reflections, saliva, restoration margins, and subtle early demineralization. We propose WaveViT-YOLO, a hybrid architecture built on a YOLOv9m backbone and integrating (i) a discrete wavelet transform (DWT) preprocessing stage, (ii) learnable WaveletAttention modules at the feature-pyramid scales, and (iii) ViT-based MultiScaleCrossAttention fusion. On the publicly available Annotated Intraoral Image Dataset (6313images; patient-level 70/15/15 split; three independent seeds), YOLOv9m is the strongest standalone YOLO model by mAP@50 (mAP@50 = $0.807\pm 0.003$; mAP@50–95 = $0.642\pm 0.004$). WaveViT-YOLO achieves the highest measured mAP@50 ($0.814\pm 0.005$; $0.007$ absolute and $+0.87$% relative), mAP@50–95 ($0.647\pm 0.004$), F1 ($0.831\pm 0.005$), and PR-AUC ($0.845$) among the evaluated models. The model contains 26.3 M parameters, a $30.8$% increase over the 20.1 M YOLOv9m baseline. Model-only inference is $27.3\pm 1.6$ ms on an NVIDIA T4 GPU, while the current CPU DWT stage adds $235.2\pm 8.0$ ms, giving approximately 262.5 ms/image end-to-end; therefore, the current pipeline is not real-time end-to-end. Using the displayed seed-averaged mAP@50 values, the isolated relative changes are $+0.62$% for DWT and $+0.37$% for either WaveletAttention or ViT fusion, whereas the full configuration reaches $+0.87$%. The paired three-seed comparison against YOLOv9m yields $t\left(2\right)=6.06$, $p=0.026$, and Cohen’s $d_z=3.50$; because $n=3$, this analysis is treated as exploratory. Small lesions (<$0.098$% image area) remain the principal limitation (recall = $0.477$). Because evaluation uses clinician-provided annotations from one retrospective dataset and no independent external or prospective validation was completed, the system is presented as a research-stage screening architecture rather than a clinically validated diagnostic tool. Full article

This study presents a short-term outdoor comparison of mirror-glass and AISI 304 stainless-steel reflectors in parabolic trough collectors equipped with identical helical copper absorbers. Both configurations were operated simultaneously using the same collector geometry, fixed inclination angle, water-supply arrangement, measurement schedule, and instrumentation. Solar irradiance, inlet and outlet water temperatures, absorber temperature, and reflector temperature were recorded over three consecutive experimental days, namely 24–26 October 2025. The results were evaluated using temperature rise and time-dependent temperature output because the gravity-assisted system was not equipped with a flow meter or active flow-control device, preventing reliable calculation of useful heat gain and thermal efficiency. The descriptive results showed that the mirror-glass configuration produced a modestly higher overall temperature response and lower variation among the three daily mean values, although it did not outperform stainless steel at every measurement time or in every daily average. The observed difference is interpreted primarily in terms of the expected higher specular reflectivity and lower optical scattering of mirror glass, which can increase the solar radiation intercepted by the absorber. However, the conclusions are limited by the three-day testing period, absence of verified mass-flow data, lack of direct reflectivity measurements, and unquantified cosine losses associated with fixed operation without automatic tracking. The findings therefore provide configuration-specific guidance for reflector selection rather than a generalized ranking of collector performance. Full article

Mechanical component detection in industrial scenes is challenged by cluttered backgrounds, large-scale variation, specular reflection, high inter-class similarity, and class imbalance. To address the above problems, this paper proposes a lightweight YOLO-style detector that integrates a PP-HGNetV2 tiny backbone, an enhanced normalization-based attention module (ImNAM), and an improved complete intersection-over-union loss (ImCIoU). The HGNetV2 backbone enhances hierarchical multi-scale feature extraction and keeps the deployable computational complexity low. ImNAM has been modified to enhance discriminative representation by introducing dual-statistics channel weighting, orthogonal edge-aware spatial modeling and bipolar adaptive residual gating. ImCIoU enhances the accuracy of localization by combining quality-aware box scaling, scale-sensitive modulation and dynamic IoU-guided weighting. A class-balancing augmentation pipeline was applied to the four-category industrial dataset of Bearing, Bolt, Gear and Nut. All experimental results are reported as the mean ± standard deviation of five independent two-tailed training runs with different random seeds, and statistical significance is verified by paired t-tests (p < 0.05) with Bonferroni correction for multiple comparisons. Experimental results show that the proposed method achieves 90.82 ± 0.35% mean average precision (mAP@0.5), 91.95 ± 0.42% precision, and 82.98 ± 0.51% recall, outperforming nine mainstream lightweight detectors, including the latest YOLOv12n (2025) and RT-DETR-tiny. Extended evaluation on mAP@0.5:0.95, per-class AP and F1 score further confirms the advantages in localization accuracy and classification performance. Ablation studies confirm that the HGNetV2 family backbone provides the largest performance gain, while the improved attention mechanism and regression loss further enhance localization accuracy and robustness. With only 4.44 M parameters and 9.96GFLOPs, the proposed detector has achieved a good accuracy–efficiency trade-off and shows strong potential for intelligent industrial inspection on resource-constrained platforms, subject to further hardware-level deployment verification. Full article

Spherical/aspherical mirrors are widely used in satellite communication and imaging systems, but their reflectivity is affected by the surface roughness value. The vibration of the machine tool system is one of the most important factors affecting the surface roughness. This paper systematically suppresses vibration through different ways, reduces the peak and valley value of workpiece surface roughness, and improves the specular reflectivity (i.e., zero-order diffraction efficiency) of the machined surface. This paper establishes a reflectance model for machined surfaces considering surface aberration using rigorous coupled wave theory. Based on this model, the relationship between reflectivity and processed surface morphology was calculated. According to the influence of different vibration modes on surface morphology, the influence of different vibration morphology on reflectivity is studied. In order to reduce cutting vibration, a new adaptive interpolation point planning algorithm has been innovatively proposed for planning the position of each interpolation point on the meridian of the workpiece. The specular reflectivity of uncoated bare spherical/aspherical mirror surfaces processed by adaptive interpolation point planning algorithm can reach over 90%, providing a high-quality substrate for subsequent high-reflection optical coatings. Full article

This paper presents a non-specular reflection-based mechanically steered reflectarray antenna (RA) that achieves ultra-wide beam steering up to 80° while preserving mainlobe integrity. Conventional mechanically steered RAs are fundamentally constrained by specular reflection, where the reflection angle follows the incident angle symmetrically. Consequently, large scan angles inherently require extreme incident angles, which exacerbate aperture projection loss, element angular instability, and beam distortion. To overcome this limitation, an asymmetric reflection phase distribution is engineered to decouple the reflection angle from the incident angle, thereby relaxing the incident angle requirement for wide-angle beam steering. By mitigating extreme angular excitation of the unit cells, the proposed approach enhances angular stability and improves beam robustness at large scan angles. Full-wave simulation and measurement demonstrate stable radiation patterns without mainlobe splitting up to a scan angle of 80°. Although a gain reduction of approximately 6 dB is observed at the maximum steering angle due to intrinsic aperture projection effects, the mainlobe characteristics remain well preserved. The proposed approach provides a physically grounded and practical pathway toward ultra-wide-angle beam steering in mechanically steered RAs. Full article

Solder-joint detection and localization on large, complex printed circuit boards (PCBs) remain challenging because PCB images often contain unevenly distributed features, nonuniform illumination, specular reflection, scale variation and geometric distortion. Conventional SIFT-based registration methods usually use fixed matching parameters and therefore cannot adapt well to regions with different component densities. To address this limitation, this study proposes a hardware–software integrated PCB image registration framework that combines a robotic end effector with a locally adaptive K-nearest neighbor (LAKNN) strategy and SIFT descriptors. The hardware platform provides stable image acquisition through a lifting mechanism and ring-light illumination, while the software module adjusts the neighbor-search space according to local feature density and matching confidence. Experimental results show that the proposed LAKNN method achieves 90.8% inlier-match accuracy in the ablation experiment and improves registration robustness under height, region and viewpoint variations. The proposed framework provides a practical basis for automated PCB inspection and robotic soldering alignment. Full article

The development of transparent multifunctional coatings capable of combining optical properties with mechanical durability remains a significant challenge in advanced materials engineering. In this study, novel hybrid coatings based on a poly(vinyl butyral)/epoxy resin (PVB/epoxy) matrix reinforced with tungsten disulfide (WS₂) nanostructures were developed and examined for potential application in camouflage protection of glass surfaces. Camouflage aims to reduce the detectability of an object by minimizing the optical contrast between the object and its surrounding environment. For transparent substrates such as glass, this objective is particularly demanding because the transparency must be preserved while reducing unwanted surface reflection and optical signatures over relevant spectral ranges. For this purpose, in this research two types of nanostructures were investigated: fullerene-like nanoparticles (IF-WS₂) and inorganic nanotubes (INT-WS₂). The coatings were fabricated via ultrasonically assisted solution dispersion followed by casting over the glass plates and Teflon molds, and solvent evaporation. Structural, thermal, optical, and mechanical properties were systematically evaluated using SEM, FTIR, DSC, UV-Vis-NIR spectroscopy, gloss measurements, hardness testing, and cavitation wear resistance analysis. The incorporation of WS₂ nanostructures led to improved mechanical performance, with increased hardness and enhanced resistance to cavitation-induced wear. Optical characterization showed moderate reductions in reflectance and controlled transmittance in the visible and near-infrared regions, while overall transparency was maintained. The results indicate that WS₂ nanostructures contribute to both light scattering and absorption, leading to reduced specular reflection and improved optical masking potential. The findings demonstrate that hybrid PVB/epoxy/WS₂ coatings offer a promising approach for designing transparent, mechanically resistant coatings with tunable optical properties, with potential applications in protective glass systems and advanced functional surfaces. Full article

Freeze–thaw (F/T) processes play a critical role in the regulation of soil hydrothermal dynamics, land–atmosphere energy exchange, and ecosystem functioning. The spaceborne global navigation satellite system reflectometry (GNSS-R) has shown great potential for land surface F/T state detection; however, its monitoring capability remains limited by spatial resolution, revisit interval, observation coverage, and complex land surface conditions. In this study, Tianmu-1 (TM-1) multi-GNSS-R observations were used to detect daily land surface F/T states over the mid-to-high latitudes of the Northern Hemisphere. First, surface reflectivity observations from multi-GNSS, including the Global Positioning System (GPS), BeiDou Navigation Satellite System (BDS), Galileo, and GLONASS, were fused using a weighted averaging method based on the number of specular reflection points. Then, TM-1 multi-GNSS-R reflectivity was used as the primary remote-sensing input, while vegetation water content (VWC), surface roughness, and snow cover information were introduced as auxiliary environmental variables. The Soil Moisture Active Passive (SMAP) F/T product was used to provide supervised reference labels for developing Bayesian-optimized extreme gradient boosting (XGBoost) models for F/T state classification. Evaluation against SMAP F/T reference labels showed that the multi-GNSS fusion model achieved an area under the curve (AUC) of 0.853 and an overall accuracy of 77.3% without incorporating snow cover information, outperforming the single-GNSS models. After incorporating snow cover information, the AUC increased to 0.959, and the overall accuracy reached 89.3%. Shapley additive explanations (SHAP) analysis further showed that snow cover made the largest contribution to the final model output, suggesting that its improvement effect may reflect both physical snow-related surface information and seasonal contextual information. An independent point-based comparison with in situ observations from the international soil moisture network (ISMN) showed that the TM-1 F/T classification accuracy reached 85.2% after incorporating snow cover information, which was comparable to that of the SMAP product. These results demonstrate that TM-1 multi-GNSS-R observations have promising potential for detecting land surface F/T states during the autumn–winter freezing development period, and that integrating multi-GNSS-R reflectivity with snow cover information can substantially improve classification performance and spatial consistency within the available observation period. Full article

Reliable three-dimensional (3D) reconstruction from endoscopic video is essential for endoscopic digital twins, scene review, and minimally invasive visual analysis. However, endoscopic images are not clean observations of intrinsic tissue appearance. Depth-dependent blur, shallow mucosal color diffusion, wet-surface specular reflection, and frame-wise color variation are often coupled with the captured signal. When such observation-dependent effects are directly optimized as Gaussian colors, conventional 3D Gaussian Splatting may encode transient imaging artifacts as persistent tissue appearance, leading to blurred textures, color drift, specular residues, and unstable novel-view synthesis. This paper presents EndoDGS (Endoscopic Degradation-Decoupled Gaussian Splatting), a degradation-decoupled Gaussian Splatting framework for endoscopic novel-view reconstruction. The core idea is to keep stable geometry and base tissue appearance in the Gaussian representation, while modeling endoscope-induced degradations separately in a bounded render-space compensation pipeline. EndoDGS combines lightweight appearance modulation for frame-wise color stabilization with sequential degradation compensation for optical blur, mucosal color transport, and wet-surface specular response. This design reduces the entanglement between persistent tissue appearance and transient imaging degradations without changing the underlying Gaussian geometry and visibility ordering. Experiments on synthetic colonoscopy and real endoscopic/laparoscopic datasets covering 38 scenes show that EndoDGS consistently improves reconstruction quality over representative implicit and explicit reconstruction baselines. The results demonstrate that separating stable tissue representation from observation-dependent endoscopic degradations provides a more faithful, stable, and interpretable foundation for endoscopic 3D reconstruction. Full article

Colorectal cancer remains a major cause of cancer-related mortality, and missed adenomas during colonoscopy are still a persistent clinical concern. Video-based computer-aided diagnosis can support polyp recognition, but real colonoscopy clips often include frame-specific degradation from motion blur, specular reflection, sensor noise, and compression. Standard temporal models usually aggregate sampled frames with similar confidence, so a few corrupted frames can distort the clip representation. This paper presents QBi-SSM, a reliability-guided bidirectional state space framework for clip-level polyp classification. QBi-SSM pairs a Frame Reliability Filter (FRF), which estimates frame reliability from sharpness, motion, and entropy cues and suppresses unreliable features, with a Bidirectional Context State Space Module (BC-SSM), which aggregates temporal evidence in both directions. The goal is to keep lightweight temporal modeling stable when frame quality varies within an endoscopic video. Here, “clean-set” denotes evaluation on the unmodified test clips before any synthetic degradation is applied. Experiments on LDPolypVideo and HyperKvasir show competitive clean-set performance rather than large clean-set gains, and both controlled synthetic degradation tests and an analysis of naturally degraded subsets show that the robustness advantage is more pronounced when frame quality is uneven. Ablation studies analyze the effects of reliability filtering, feature alignment, bidirectional scanning, and the loss formulation. Full article

Handwritten identification marks on steel plate end faces are important for material traceability, inventory checking, and production scheduling in large steel storage yards. Their localization remains challenging because field images often contain uneven illumination, specular reflection, rust, oil contamination, densely adjacent targets, and arbitrary target orientations. This study presents an application-oriented pipeline for oriented localization of handwritten marks on densely stacked steel plates. The pipeline combines task-oriented image enhancement, high-resolution sliding-window inference, and a YOLOv11 oriented bounding-box detector (YOLOv11-OBB) adapted with attention enhancement, adjusted multi-scale fusion, and Scylla-IoU (SIoU)-based box regression. A real steel-yard dataset was annotated with oriented bounding boxes and split at the source-image level before patch extraction. The held-out evaluation subset was used both for checkpoint selection and final performance reporting under the same protocol. On this dataset, the final detector achieved 89.9% precision, 88.3% recall, 91.8% mAP@0.5, and 72.0% mAP@0.5:0.95. These results support the practical value of oriented localization and high-resolution inference for weak handwritten regions in complex industrial scenes. Broader detector benchmarking and downstream recognition performance remain to be validated in future work. Full article

Industrial defect generation has long been challenged by the scarcity of real anomaly samples and the imbalance of defect categories, particularly in complex industrial scenarios involving transparent containers. Taking vials as an example, glass reflection, specular highlights, and fine-grained defects make continuous defect acquisition difficult, thereby making the realism and controllability of augmented samples critical to downstream detection performance. Although existing diffusion-based generation methods can improve synthetic image quality, they often require additional training or lightweight fine-tuning, which limits their efficiency in sample-limited industrial scenarios. To address this issue, this paper builds upon the TF-IDG framework and proposes a training-free industrial defect generation method based on multi-domain consistency and geometric-semantic constraints. To alleviate the unnatural texture details, boundary transitions, and background blending commonly observed in generated defects, a multi-domain consistency constraint is introduced to enhance generation realism from both frequency-domain structures and cross-domain contextual representations, thereby improving anomaly texture expression and overall visual coherence. To further mitigate unstable defect contours, spatial deviation, and structural mismatch with target objects, a geometric-semantic constraint is designed to regulate the generation process through elastic shape constraints and semantic region-anchored attention, enhancing the rationality of defect morphology evolution and spatial localization. Experimental results on both the MVTec AD dataset and a self-built vial defect dataset demonstrate that the proposed method outperforms comparative approaches. Specifically, when YOLOv11 is used as the downstream detector, the mAP@50 on the MVTec AD dataset and the self-built vial defect dataset is improved from 88.5% and 98.0% for the TF-IDG baseline to 89.6% and 98.8%, respectively. Full article

Terrestrial laser scanners provide both geometric and radiometric information, and terrestrial laser scanning (TLS) intensity is influenced by surface optical properties and the angle of incidence. This study presents a piecewise polynomial Lambert–Beckmann (PPLB) model to enhance TLS intensity characterization for broad-leaved surfaces. The PPLB model incorporates the Beckmann law, thus enabling a data-driven estimation of transition angles and enhancing the fitting flexibility across diverse leaf surfaces. TLS measurements were conducted on adaxial and abaxial leaf surfaces of five tree species using a RIEGL VZ-400 scanner. The results indicated that the TLS intensity consistently declined as the angle of incidence increased; however, different species and surfaces showed distinct angular response patterns. Across all ten tested species–surface combinations, the PPLB model achieved low root mean square error values of 0.0095–0.0183 and yielded three physically meaningful parameters: specular reflection contribution ($k_s$), surface roughness ($m$), and transition angle (${\theta }_T$). Excluding the ${\theta }_T$ = 0° fitted result for the Golden Shower Tree abaxial surface, the estimated nonzero ${\theta }_T$ values ranged from 30° to 64°, indicating substantial variability in threshold angles among leaf surfaces beyond the commonly assumed value of 45°. These findings highlight the importance of incorporating surface-specific threshold angles for improved leaf characterization. Full article