Search Results (199)

Maritime aerial imaging is strongly affected by rapid illumination variations induced by dynamic sea conditions, which often cause conventional exposure control approaches to misinterpret intrinsic scene brightness as overexposure resulting from elevated camera settings. To overcome this issue, an adaptive exposure control framework based on a Glare-Aware Attention Network is proposed, implemented within an end-to-end dual-branch architecture. The framework utilizes an Exposure State Encoding (ESE) module to encode the current frame’s exposure parameters as conditional vectors, thereby resolving physical ambiguities in scene understanding. A Glare-Aware Spatial Attention (GASA) mechanism is further introduced, incorporating a glare prior map (GPM) generated using a “high-luminance, low-texture” heuristic to explicitly suppress sun glint effects. A Scene Difficulty-Adaptive Loss Weighting (SDAW) scheme is designed to adaptively regulate loss weights, and region-aware evaluation metrics, KREA and ISR, are defined. On a self-collected maritime aerial imaging dataset, the proposed approach significantly outperforms both traditional and deep learning-based methods in terms of full-frame and region-level performance metrics. Compared with the multi-task CNN baseline that has the closest parameter count, it achieves a 1.7 dB gain in PSNR. Cross-dataset validation on SeaDronesSee, temporal consistency analysis, and embedded platform testing further support the generalization and real-time feasibility of the proposed solution. Offering a high-accuracy, region-aware exposure control solution for aerial cameras in complex sea surface scenarios. Full article

This study investigates the influence of inside-trapped water and remedial grouting on the vertical bearing behaviour of suction bucket foundations in sand through 1 g laboratory model tests. The tests were designed to compare the relative responses of different trapped-water and grouting conditions under the same model scale, sand preparation procedure, and loading protocol. Two target trapped-water conditions were considered: a condition without an observable continuous water layer beneath the bucket lid and a condition with an initial trapped-water thickness of approximately 2 cm. These conditions were controlled and verified before loading using the scale attached to the transparent bucket wall and the underwater camera monitoring system. The results show that inside-trapped water modifies the vertical load-transfer path between the bucket lid and the internal soil plug. When a water layer exists beneath the lid, direct lid–soil plug contact is weakened, and the foundation resistance relies more strongly on skirt-side resistance and the resistance mobilized near the bucket rim. Under cyclic vertical loading, the trapped-water case exhibited larger cumulative displacement and a lower post-cyclic bearing response than the no-trapped-water case. The secant cyclic stiffness showed a continuous increase in the no-trapped-water case, whereas a rise-then-fall trend was observed in the trapped-water case, which may be associated with cyclic densification, soil plug disturbance, changes in lid–soil plug contact, and possible local pore pressure development. Remedial grouting filled the trapped-water space beneath the bucket lid and partially restored the lid–soil plug load-transfer path. Under the present model test conditions, the post-cyclic dimensionless bearing capacity of the grouted cases increased by approximately 13–16% relative to the ungrouted trapped-water case. The grouting cases with different bentonite contents showed similar recovery trends within the limited dataset, suggesting that the improvement was mainly related to filling and sealing the trapped-water space rather than to the intrinsic strength of the grout material. Full article

Monocular metric depth estimation models increasingly incorporate camera intrinsics or learn camera-aware representations to recover physically meaningful scales. However, existing camera-aware studies have paid limited attention to a practical deployment gap: common crop, resize, and padding operations alter the image coordinate system, but inference pipelines may still pass stale camera intrinsics to the model. This image–intrinsics inconsistency produces a crop-resize-induced intrinsics mismatch, which can lead to systematic depth bias and degraded geometric consistency. We show that once preprocessing parameters are fixed, the effective intrinsics are determined by a parametric affine mapping, enabling resize-induced focal-length scaling errors and crop-induced principal-point shifts to be analyzed separately. We further distinguish this parameter-conditioned mismatch from broader calibration uncertainty caused by noisy intrinsics, noisy preprocessing metadata, or missing metadata. Based on this formulation, we introduce a controlled evaluation protocol and a lightweight Mismatch-Aware Camera Module (MACM) that combines preprocessing metadata with image-derived camera cues to condition intermediate depth features. In our ablation study, MACM with the proposed consistency loss reduces the mismatched Abs.Rel from 0.141 to 0.114 and narrows the robustness gap from 0.038 to 0.017, while preserving accuracy under matched preprocessing. These results indicate that treating the image and intrinsics as a coupled representation is essential for robust monocular metric depth estimation in practical preprocessing pipelines. Full article

The activity of an Apennine wolf (Canis lupus italicus) family inhabiting the natural reserve Calanchi di Atri in central Italy was monitored during the post-reproductive period (May–October) of two consecutive years (2023–2024), using ten camera trap sites. Detections were classified into adults and pups. Although records cover a limited period and focus on a single pack, they allowed the detection of variations in the spatiotemporal activity of the wolf family. In the first year, wolf activity peaked in summer, with adults frequently supervising pups at rendezvous sites. In the second year, activity by both adults and pups declined significantly and was accompanied by an evident shift in territory use. In addition to potential intrinsic factors, such as individual variability and litter dynamics, these variations may also reflect increased environmental stressors and anthropogenic disturbance. These findings provide insights into how wolves adapt their behavior in human-modified landscapes and highlight the importance of integrating human–wildlife dynamics into conservation and management strategies. Full article

The study focuses on the infrared nondestructive detection of inclusion-type internal defects in glass-fiber-reinforced plastic (GFRP) wind turbine blade specimens, which were designed to simulate the laminated material structure and typical hidden defects of in-service blades. To address the difficulty of detecting internal defects in in-service wind turbine blades, this paper establishes an active thermal imaging defect detection and recognition system using a halogen lamp as the infrared thermal excitation source and a high-resolution thermal imaging camera as the detection component. To improve the recognition of defect contour information in infrared images, a method combining bidimensional filtering empirical mode decomposition (BFEMD), Gaussian filtering, and Otsu threshold segmentation is proposed. The BFEMD procedure decomposes the infrared image into bidimensional intrinsic mode function components and residual components, Gaussian filtering suppresses noise in the selected components, and Otsu threshold segmentation extracts the defect contours. Experimental results show that the combined algorithm can enhance defect targets in infrared images, improve visibility and contour integrity, and provide a higher detection rate for wind turbine blade defects under different defect depths and materials. Full article

Image stitching is a computational technique designed to align and seamlessly fuse multiple overlapping images into a single panoramic image with an extended field of view. It plays a critical role in diverse domains, including mobile photography, autonomous navigation, and visual perception systems. However, most conventional image stitching pipelines implicitly assume that the input images have been pre-corrected for geometric distortions, particularly radial distortion inherent to wide-angle and fisheye lenses. This assumption often fails in practice, as many consumer-grade cameras lack built-in correction or calibration support. Consequently, applying standard image stitching methods to the uncorrected imagery frequently degrades feature correspondence reliability and introduces visible geometric misalignments and seam discontinuities in the final panorama. To overcome these limitations, this paper introduces a task-driven joint iterative optimization framework for image stitching that unifies unsupervised radial distortion correction, distortion-aware feature registration, and seam-aware blending within a single cohesive optimization objective. Specifically, lens distortion parameters are explicitly modeled as learnable variables and embedded into both the geometric registration and seam optimization sub-problems. An efficient closed-loop optimization strategy is then employed to jointly refine distortion parameters, homography estimates, and optimal seam paths in an alternating, mutually reinforcing manner. Implementation-wise, we first propose a calibration-free initial radial distortion estimation method which leverages intrinsic image gradients and epipolar consistency to provide physically plausible initialization for subsequent optimization. During iteration, distortion parameters are progressively refined by integrating robust geometric constraints derived from current feature matches (via RANSAC-based consensus filtering) with photometric consistency cues. Extensive experiments on multiple public benchmarks featuring pronounced radial distortion demonstrate that our method achieves superior stitching fidelity using metrics including PSNR and SSIM. It also confirms enhanced feature matching stability, which outperforms both distortion-agnostic approaches and two-stage pipelines that decouple distortion correction from registration. Furthermore, comprehensive ablation studies quantitatively and qualitatively validate the functional necessity and synergistic contribution of each core module, confirming the design rationale and effectiveness of the proposed joint optimization architecture. Full article

Remote photoplethysmography (rPPG) estimates heart rate by analyzing subtle blood-flow-induced color variations from camera videos; however, its performance is highly sensitive to illumination changes caused by variations in light intensity, position, and environmental conditions. To address this limitation, this study proposes a lightweight, training-free brightness normalization method that suppresses illumination-induced luminance fluctuations while preserving physiologically relevant color variations associated with blood perfusion. The proposed approach separates luminance and chrominance components from the frame-mean RGB vector and applies normalization only to the brightness component, thereby maintaining the intrinsic color direction essential for rPPG signal extraction and stabilizing temporal brightness without distorting chrominance relationships. Experimental evaluations show that channel-wise mean values vary only within $\pm 6$–$12\%$ with negligible changes in standard deviation, while dynamic range and temporal stability are significantly improved. Furthermore, when combined with an SNR-based signal selection strategy, the proposed method reduces the mean absolute error (MAE) of the CHROM algorithm on the DLCN dataset from approximately 18–19 BPM to 4.87 BPM under complex illumination scenarios, with consistent improvements also observed on the MR-NIRP dataset. These results suggest that the proposed preprocessing method helps preserve blood-flow-induced temporal color variations and improves the robustness of rPPG measurement under diverse illumination conditions. Full article

The daily activity patterns of wild animal species are driven by environmental conditions and plant productivity although the degree of dependence varies according to their ecological niche. Bear ecology is intrinsically linked to seasonal vegetative availability. As omnivores with high metabolic demands, these species rely heavily on botanical resources including fruits, seeds, and roots. Consequently, differences in primary productivity across the landscape influence how individuals distribute their circadian activity patterns. The Enhanced Vegetation Index (EVI) is a tool that quantifies the quality and vigor of vegetation. Relating the EVI to activity patterns allows us to understand how vegetation dynamics and conditions influence the use of time at different times of the day. This study analyzes the daily activity pattern of the American black bear (Ursus americanus) in the El Cielo Biosphere Reserve (ECBR) using camera traps and its association with spatial variations in the Enhanced Vegetation Index (EVI). The results show that the daily activity pattern of the American black bear in the ECBR exhibits a diurnal–crepuscular tendency. In areas with high primary productivity and higher temperatures, activity occurs before sunrise and at sunset, with low activity during the rest of the day. In contrast, in areas with less vegetation and lower temperatures, activity occurs throughout the day. This suggests that, in the ECBR, the activity pattern of black bears could be modulated by temperature variations related to changes in vegetation productivity. Full article

Phase-shifting fringe projection (PSFP) is widely used in industrial inspection and three-dimensional measurement, where $\gamma$ nonlinearity of the projector–camera system is traditionally treated as a phase-error source to be calibrated or compensated. In this work, $\gamma$ nonlinearity is reinterpreted from an imaging perspective and shown to act as a statistical distortion mechanism that reshapes modulation stability, overexposure behavior, and defect saliency in fringe-based imaging. Building on the intrinsic DC–AC decomposition of phase-shifting demodulation, we analyze how $\gamma$ nonlinearity interacts with fringe modulation and frequency-selective transfer. An analytical model reveals that $\gamma$ nonlinearity simultaneously suppresses the fringe fundamental and introduces harmonic leakage, leading to systematic compression of mean modulation contrast in high-brightness regions. As a result, $\gamma$ correction does not necessarily enhance mean-based defect contrast and may even reduce it, contrary to common intuition. We further demonstrate that the primary benefit of $\gamma$ correction lies in statistical stabilization rather than contrast amplification. By introducing modulation-domain saliency formulations and a frequency-domain harmonic energy ratio, a physical link is established between $\gamma$ nonlinearity, overexposure, and defect separability. Controlled experiments on highly reflective sheet-metal specimens confirm that while mean-contrast- and SNR-based saliency metrics often decrease after $\gamma$ correction, separability-based metrics consistently improve due to reduced nonlinear- and saturation-induced variance. Cross-channel and cross-condition analyses further show that modulation and reflectance images respond differently to $\gamma$ correction, yet metric-level separability exhibits consistent improvement across channels. These results clarify the true role of $\gamma$ correction in fringe-based inspection and provide theoretical insight and practical guidance for robust defect imaging under nonlinear and near-overexposure conditions. Full article

To address the calibration challenge caused by the nonlinear variation in intrinsic parameters during continuous camera zooming in UAV electro-optical pods, this paper proposes an online calibration method based on IMU-visual fusion. Traditional offline calibration cannot adapt to dynamic scenarios, while existing self-calibration methods suffer from slow convergence and insufficient robustness. The proposed method aims to achieve real-time and accurate estimation of camera intrinsic parameters during zooming. Specifically, we first construct a unified state estimation framework that encodes the internal and external parameters of the camera and the 3D positions of scene feature points into a high-dimensional state vector, then establish a camera motion model based on IMU data, construct a visual observation model by combining the pinhole camera and second-order radial distortion model to establish a nonlinear mapping from 3D feature points to 2D pixel coordinates, and adopt an improved ORB algorithm for feature extraction and LK optical flow method to achieve high-precision cross-frame feature matching to enhance the stability of visual observation. Most importantly, we design a tight-coupling fusion strategy based on the Extended Kalman Filter (EKF) prediction-update iteration mechanism, which fuses IMU high-frequency motion constraints and visual geometric constraints in real time to suppress parameter drift induced by focal length changes. Finally, we recursively solve the state vector to complete the online dynamic estimation of intrinsic parameters. Monte Carlo simulation experiments and real UAV flight experiments confirm that the method has both high estimation accuracy and strong environmental adaptability, can meet the high-precision calibration needs of UAVs in dynamic scenarios, and provides reliable technical support for accurate target positioning. Full article

Three-dimensional reconstruction using unmanned aerial vehicle (UAV) platforms has been extensively utilized in various fields. While conventional techniques such as oblique photogrammetry can produce mesh models with geographical references, they often require substantial computational resources. Although recent studies have attempted to incorporate camera pose parameters into the emerging 3D Gaussian Splatting (3DGS), these methods often treat georeferencing as a post-processing step or rely on global bundle adjustment, which may propagate systematic errors and compromise final accuracy. This work integrates georeferencing as an intrinsic constraint during 3DGS training, enabling simultaneous optimization of geographic and photometric accuracy. The core of our approach lies in introducing a similarity transformation matrix T connecting the local model space with the global geographic coordinate system, along with a dedicated geographic loss function. Geographic coordinates are transformed via T before reprojection to compute the loss function. It was demonstrated that GeoRefGS presents a viable solution for efficiently integrating georeferenced information into 3DGS. Indeed, the proposed framework achieves an improvement of approximately 3.31 dB in peak signal-to-noise ratio while maintaining distance errors below 0.054 m, enabling reliable geographically referenced 3D reconstruction in substantially less time compared to conventional photogrammetric approaches. Full article

In the aviation industry the so-called ballistic impact of small accidental or human-made sources on aircraft elements during their service life encompasses several scenarios of practical interest. The experimental assessment of ballistic impact requires dedicated infrastructures (such as the light-gas gun system utilized in this study) and exhibits intrinsic difficulties, mainly concerning the proper acceleration of a projectile and the accurate measurement by a high-speed camera of its (inlet and outlet) velocity. As a first objective, this study aimed at characterizing the dynamic response of fiber metal laminates, manufactured ad hoc by the authors with two different stacking sequences currently not available in commerce. The layups included aluminum 2024 T3 and aramid fiber-reinforced prepregs, leading through specific treatments to excellent specific properties. The collision of the laminate with a 25 g, 9 mm radius steel sphere, traveling at speeds ranging from 90 to 145 m/s, caused a variety of scenarios: partial or complete penetration, with the projectile passing through and continuing its trajectory, remaining stuck in the sample (embedment) or even being bounced back (ricochet). The experimental information led to the estimation, for each typology of sample, of a conventional ballistic limit according to the Lambert-Jonas approximation, as a second objective, these data were utilized to validate an accurate heterogeneous model of the samples developed in the ABAQUS^® platform, discretized by finite elements in explicit dynamics and including geometric nonlinearity and contact. We describe plasticity and damage of the metal layers by the Johnson–Cook phenomenological model, progressive failure in the fiber-reinforced plies through a 2D Hashin criterion with damage evolution, and interlaminar debonding at multiple cohesive interfaces governed by the Benzeggagh–Kenane criterion. The outlet speed of the bullet measured during the experiments was retrieved correctly by this model, and a satisfactory agreement of the finite element predictions was found with the deformation patterns and the damage mechanisms identified by post mortem visual inspection. Finally, several discussion points are raised, concerning the robustness of the numerical analyses, the reliability of the constitutive modeling and the identification of the governing parameters. Full article

High-fidelity 4D reconstruction of dynamic scenes is pivotal for immersive simulation yet remains challenging due to the photometric inconsistencies inherent in multi-view sensor arrays. Standard 3D Gaussian Splatting (3DGS) strictly adheres to the brightness constancy assumption, failing to distinguish between intrinsic scene radiance and transient brightness shifts caused by independent auto-exposure (AE), auto-white-balance (AWB), and non-linear ISP processing. This misalignment often forces the optimization process to compensate for spectral discrepancies through incorrect geometric deformation, resulting in severe temporal flickering and spatial floating artifacts. To address these limitations, we present Lumina-4DGS, a robust framework that harmonizes spatiotemporal geometry modeling with a hierarchical exposure compensation strategy. Our approach explicitly decouples photometric variations into two levels: a Global Exposure Affine Module that neutralizes sensor-specific AE/AWB fluctuations and a Multi-Scale Bilateral Grid that residually corrects spatially varying non-linearities, such as vignetting, using luminance-based guidance. Crucially, to prevent these powerful appearance modules from masking geometric flaws, we introduce a novel SSIM-Gated Optimization mechanism. This strategy dynamically gates the gradient flow to the exposure modules based on structural similarity. By ensuring that photometric enhancement is only activated when the underlying geometry is structurally reliable, we effectively prioritize geometric accuracy over photometric overfitting. Extensive experiments validate the quantitative superiority of Lumina-4DGS. On the Waymo Open Dataset, our method achieves a state-of-the-art Full Image PSNR of 31.12 dB while minimizing geometric errors to a Depth RMSE of 1.89 m and Chamfer Distance of 0.215 m. Furthermore, on our highly challenging self-collected surround-view dataset featuring severe unconstrained illumination shifts, Lumina-4DGS yields a significant 2.13 dB PSNR improvement over recent driving-scene baselines. These results confirm that our framework achieves photorealistic, exposure-invariant novel view synthesis while maintaining superior geometric consistency across heterogeneous camera inputs. Full article

In this work, thermal imaging is employed to study the opto-thermal response of apples (Malus domestica Borkh.), assessing their post-harvest evolution through the estimation of thermal diffusivity. A non-destructive experimental procedure based on mid-wave infrared (MWIR) thermal camera (3–5 µm) and localized heating with a visible laser is developed, enabling spatially and temporally resolved surface temperature measurements. Temperature fields are recorded at different time points and radial distances from the heated spot. A theoretical model based on Fourier thermal diffusion equation is formulated to describe the spatio-temporal evolution of surface temperature. After validation on a reference sample, the method is applied to Golden and Red Delicious apples over a 28-day storage period at room temperature. Red Delicious apple exhibits higher mean diffusivity values without significant temporal changes, whereas a progressive increase in diffusivity is observed for Golden Delicious apples. These results show that thermal diffusivity is sensitive to post-harvest physiological changes in apple tissue and may be associated with intrinsic properties such as tissue density and water content. By relating laser-induced temperature fields to the estimation of thermal diffusivity, this approach enables the non-destructive, quantitative assessment of thermal diffusivity, showing potential for fruit maturity and quality assessment, which are of high importance in agri-food monitoring applications. Full article

Low-cost, smartphone-based thermal cameras offer unprecedented accessibility for physiological monitoring, yet their validity and reliability for absolute skin temperature measurement in clinical settings remain contentious. This study aims to quantify the agreement and repeatability of a widely used smartphone thermal camera, the FLIR One Pro, against a consumer-grade, non-contact infrared thermometer, the iHealth PT3. A method comparison study was conducted with 40 healthy adult participants, yielding a total of 2400 temperature measurements. Skin temperature of the hand dorsum was measured concurrently with the FLIR One Pro and the iHealth PT3. The protocol involved two rounds: Round 1 (R1) in a stable, static environment to assess baseline repeatability, and Round 2 (R2) in a dynamic environment mimicking clinical repositioning. The performance of the instruments was compared using paired t-tests for mean differences and Bland–Altman analysis for assessing agreement. The iHealth PT3 demonstrated superior precision, with an average intra-participant standard deviation (SD) of 0.030 °C in R1 and 0.092 °C in R2. In stark contrast, the FLIR One Pro exhibited significantly higher variability, with an average SD of 0.34 °C in R1 and 0.30 °C in R2. Bland–Altman analysis revealed a substantial mean bias of −1.42 °C in R1 and −1.15 °C, with critically wide 95% limits of agreement ranges of ≈6 °C. The substantial systematic bias and poor agreement of the FLIR One Pro far exceed both its manufacturer-stated accuracy and clinically acceptable error margins for absolute temperature measurement. To further examine whether calibration could mitigate these deficiencies, we applied a suite of ten machine learning regressors to map FLIR readings onto iHealth PT3 values. Calibration reduced systematic bias across all models, with Quantile Gradient-Boosted Regression Trees achieving the lowest MAE (1.162 °C). The Extra Trees model yielded the lowest RMSE (1.792 °C) and the highest explained variance ($R^2$ = 0.152), yet this relatively low value confirms that the device’s high intrinsic variability limits the effectiveness of algorithmic correction. As such the device has limited utility for longitudinal patient monitoring or for diagnostic decisions that rely on precise, absolute temperature thresholds. These findings inform medical practitioners in low-resource settings of the profound limitations of using this device as a standalone clinical thermometer and emphasize that algorithmic correction cannot compensate for fundamental hardware and measurement noise constraints. Full article