Search Results (193)

A monocular deflectometric system comprises a camera and a screen that collaboratively facilitate the reconstruction of a specular surface under test (SUT). This paper presents a methodology for solving the slope distribution of the SUT utilizing pose estimation derived from reflections, based on vision ray calibration (VRC). Initially recorded by the camera, an assisted flat mirror in different postures reflects the patterns displayed by a screen maintained in a constant posture. The system undergoes a calibration based on the VRC to ascertain the vision ray distribution of the camera and the spatial relationship between the camera and the screen. Subsequently, the camera records the reflected patterns by the SUT, which remains in a constant posture while the screen is adjusted to multiple postures. Utilizing the VRC, the vision ray distribution among several postures of the screen and the SUT is calibrated. Following this, an iterative integrated calibration is performed, employing the calibration results from the preceding separate calibrations as initial parameters. The integrated calibration amalgamates the cost functions from the separate calibrations with the intersection of lines in Plücker space. Ultimately, the results from the integrated calibration yield the slope distribution of the SUT, enabling an integral reconstruction. In both the numeric simulations and actual measurements, the integrated calibration significantly enhances the accuracy of the reconstructions when compared to the reconstructions with the separate calibrations. Full article

To address the challenges of manual inspection dependency, low efficiency, and high costs in evaluating the surface grinding quality of composite materials, this study investigated machine vision-based surface recognition algorithms. We proposed a multi-scale texture fusion analysis algorithm that innovatively integrated luminance analysis with multi-scale texture features through decision-level fusion. Specifically, a modified Rayleigh parameter was developed during luminance analysis to rapidly pre-segment unpolished areas by characterizing surface reflection properties. Furthermore, we enhanced the traditional Otsu algorithm by incorporating global grayscale mean (μ) and standard deviation (σ), overcoming its inherent limitations of exclusive reliance on grayscale histograms and lack of multimodal feature integration. This optimization enables simultaneous detection of specular reflection defects and texture uniformity variations. To improve detection window adaptability across heterogeneous surface regions, we designed a multi-scale texture analysis framework operating at multiple resolutions. Through decision-level fusion of luminance analysis and multi-scale texture evaluation, the proposed algorithm achieved 96% recognition accuracy with >95% reliability, demonstrating robust performance for automated surface grinding quality assessment of composite materials. Full article

To meet the requirements for radar echo acquisition and feature extraction from stratified media and rough-surfaced targets, a vehicle was geometrically modelled in CAD. Monte Carlo techniques were applied to generate the rough interfaces at air–snow and snow–soil boundaries and over the vehicle surface. Soil complex permittivity was characterized with a four-component mixture model, while snow permittivity was described using a mixed-media dielectric model. The composite electromagnetic scattering from a rough-surfaced vehicle on snow-covered soil was then analyzed with the finite-difference time-domain (FDTD) method. Parametric studies examined how incident angle and frequency, vehicle orientation, vehicle surface root mean square (RMS) height, snow liquid water content and depth, and soil moisture influence the composite scattering coefficient. Results indicate that the coefficient oscillates with scattering angle, producing specular reflection lobes; it increases monotonically with larger incident angles, higher frequencies, greater vehicle RMS roughness, and higher snow liquid water content. By contrast, its dependence on snow thickness, vehicle orientation, and soil moisture is complex and shows no clear trend. Full article

Time-of-flight sensors have emerged as a viable solution for real-time distance sensing in robotic safety applications due to their compact size, fast response, and contactless operation. This study addresses one of the key challenges with time-of-flight sensors, focusing on how they perceive and evaluate the environment, particularly in the presence of complex geometries and reflective surfaces. Using a Universal Robots UR5e arm in a controlled indoor workspace, two different sensors were tested across eight scenarios involving objects of varying shapes, sizes, materials, and reflectivity. Quantitative metrics including the root mean square error, mean absolute error, area difference, and others were used to evaluate measurement accuracy. Results show that the sensor’s field of view and operating principle significantly affect its spatial resolution and object boundary detection, with narrower fields of view providing more precise measurements and wider fields of view demonstrating greater resilience to specular reflections. These findings offer valuable insights into selecting appropriate ToF sensors for integration into robotic safety systems, particularly in environments with reflective surfaces and complex geometries. Full article

Real-time 3D reconstruction in minimally invasive surgery improves depth perception and supports intraoperative decision-making and navigation. However, endoscopic imaging presents significant challenges, such as specular reflections, low-texture surfaces, and tissue deformation. We present a novel, deterministic and iterative stereo-matching method based on adaptive support weights that is tailored to these constraints. The algorithm is implemented in CUDA and C++ to enable real-time performance. We evaluated our method on the Stereo Correspondence and Reconstruction of Endoscopic Data (SCARED) dataset and a custom synthetic dataset using the mean absolute error (MAE), root mean square error (RMSE), and frame rate as metrics. On SCARED datasets 8 and 9, our method achieves MAEs of 3.79 mm and 3.61 mm, achieving 24.9 FPS on a system with an AMD Ryzen 9 5950X and NVIDIA RTX 3090. To the best of our knowledge, these results are on par with or surpass existing deterministic stereo-matching approaches. On synthetic data, which eliminates real-world imaging errors, the method achieves an MAE of 140.06 μm and an RMSE of 251.9 μm, highlighting its performance ceiling under noise-free, idealized conditions. Our method focuses on single-shot 3D reconstruction as a basis for stereo frame stitching and full-scene modeling. It provides accurate, deterministic, real-time depth estimation under clinically relevant conditions and has the potential to be integrated into surgical navigation, robotic assistance, and augmented reality workflows. Full article

Background/Objectives: Keratoconus (KC) is a corneal ectatic disease, characterized by the progressive thinning of the cornea, myopia, and astigmatism, which lead to a decrease in visual acuity. Corneal collagen crosslinking (CXL) is an efficient method of stopping the progression of the disease. The objective of this study is to investigate the endothelial and biomechanical properties of the cornea in keratoconus patients, before and after undergoing corneal collagen crosslinking. Methods: A total of 66 eyes were diagnosed with progressive keratoconus and were recommended epi-off corneal crosslinking. Before the procedure, they were investigated with corneal topography (for minimum, maximum, average keratometry, and corneal astigmatism), specular microscopy (for the following endothelial cell parameters: number, density, surface, variability, and hexagonality), and an ocular response analyzer (for the following biomechanical parameters: corneal hysteresis and resistance factor). All measurements were repeated 1 month and 6 months after the intervention. Results: Several parameters differ according to the Amsler–Krumeich stage of keratoconus: in more advanced stages, patients present higher endothelial cell variability, a lower number of endothelial cells in the paracentral region of the cornea, lower CCT and CRF, and higher keratometry and astigmatism. Endothelial cell variability and number correlate with average keratometry, and there are also strong correlations between topography and CH and CRF. After CXL, the paracentral number of endothelial cells decreased; cell variability and average cell surface increased. Conclusions: More advanced keratoconus cases present with altered corneal biomechanics and topographical parameters, the endothelial layer also being affected proportional to the stage of the disease and also slightly affected after corneal collagen crosslinking. Full article

Users of remotely sensed Earth optical imagery are increasingly demanding a surface reflectance or surface temperature product instead of the top-of-atmosphere products that have been produced historically. Validating the accuracy of surface products remains a difficult task since it involves assessment across a range of atmospheric profiles, as well as many different land surface types. Thus, the standard approaches from the satellite calibration community do not apply, and new technologies need to be developed. The Big Multi-Agency Campaign (BigMAC) was developed to assess current technologies that might be used for the validation of surface products derived from satellite imagery, with emphasis on Landsat. Conducted in August 2021, in Brookings, SD, USA, a variety of measurement technologies were fielded and assessed for accuracy, precision, and deployability. Each technology exhibited its strengths and weaknesses. Handheld spectroradiometers are capable of surface reflectance measurements with accuracies within the 0.01–0.02 absolute reflectance units, but these are expensive to deploy. Unmanned Aircraft System (UAS)-based radiometers have the potential of making measurements with similar accuracy, but these are also difficult to deploy. Mirror-based empirical line methods showed improved accuracy potential, but their deployment also remains an issue. However, there are inexpensive radiometers designed for long-term autonomous use that exhibited good accuracy and precision, in addition to being easy to deploy. Thermal measurement technologies showed an accuracy potential in the 1–2 K range, and some easily deployable instruments are available. The results from the BigMAC indicate that there are technologies available today for conducting operational surface reflectance/temperature measurements, with strong potential for improvements in the future. Full article

An evaporation duct is a kind of atmospheric event with a refractive index exceeding the curvature of the Earth, which mostly exists on the ocean surface. Evaporation ducts have a great influence on radar, such as causing blind zones or achieving over-the-horizon detection. However, there is a lack of effective technology for evaporation duct detection, especially for passive methods. Global Navigation Satellite System Reflectometry (GNSS-R) has demonstrated potential in various remote sensing applications. However, its utilization for evaporation duct retrieval has not yet been successfully achieved. This study investigates the impact of evaporation ducts on GNSS-R delay maps (DMs), demonstrating that they elevate the non-specular point region, with the extent of this rising zone correlating with the evaporation duct height (EDH). Through semi-physical simulation, the rise signal is modeled. During a four-day experiment, GPS-R DMs with obvious features of evaporation ducts were repeatedly observed. Additionally, this study attempts to find the maximum code delay in the experimental data. The EDH is retrieved using the maximum code delay and GPS elevation angle, exhibiting a 4 m error relative to the reference model under the condition that all effective waveforms are successfully received. The results demonstrate that the GNSS-R offers a promising passive method for evaporation duct detection. Full article

Based on time-averaged digital holography, a vibration deformation measurement system was designed and a full process reconstruction and identification strategy was developed for detecting the micro-defects in optical materials. Through the double beam expansion setting and off-axis imaging adjustments, it is suitable for measuring optical materials with non-specular surfaces by double exposure shots. The scheme was applied to optical sandwich composites and 3D printed glass. Abnormal amplitudes occur at the defects due to different resonance frequencies, resulting in anomalous vibrations under excitation, and the differences in the amplitudes and phases before and after vibration can effectively characterize vibration amplitude and subsurface defects, proving that this method has a high detecting sensitivity. Full article

In industrial manufacturing, product quality is of paramount importance, as surface defects not only compromise product appearance but may also lead to functional failures, resulting in substantial economic losses. Detecting defects on complex surfaces remains a significant challenge due to the variability of defect characteristics, interference from specular reflections, and imaging non-uniformity. Traditional computer vision algorithms often fall short in addressing these challenges, particularly for defects on highly reflective curved surfaces such as aircraft engine blades, bearing surfaces, or vacuum flasks. Although various optical imaging techniques and advanced detection algorithms have been explored, existing approaches still face limitations, including high system complexity, elevated costs, and insufficient capability to detect defects with diverse morphologies. To address these limitations, this study proposes an innovative approach that analyzes the propagation of light on complex surfaces and constructs a polarization imaging system to eliminate glare interference. This imaging technique not only effectively suppresses glare but also enhances image uniformity and reduces noise levels. Moreover, to tackle the challenges posed by the diverse morphology of defects and the limited generalization ability of conventional algorithms, this study introduces a novel multi-scale edge information selection module and a Focal Modulation module based on the YOLOv11 architecture. These enhancements significantly improve the model’s generalization capability across different defect types. Experimental results show that, compared to state-of-the-art object detection models, the proposed model achieves a 3.9% increase in precision over the best-performing baseline, along with notable improvements in recall, mAP50, and other key performance indicators. Full article

Fringe projection profilometry (FPP) is a widely employed technique owing to its rapid speed and high accuracy. However, when FPP is utilized to measure shiny surfaces, the fringes tend to be saturated or too dark, which significantly compromises the accuracy of the 3D measurement. To overcome this challenge, this paper proposes an efficient method for the 3D measurement of shiny surfaces based on FPP. Firstly, polarizers are employed to alleviate fringe saturation by leveraging the polarization property of specular reflection. Although polarizers reduce fringe intensity, a deep learning method is utilized to enhance the quality of fringes, especially in low-contrast regions, thereby improving measurement accuracy. Furthermore, to accelerate measurement efficiency, a dual-frequency complementary decoding method is introduced, requiring only two auxiliary fringes for accurate fringe order determination, thereby achieving high-efficiency and high-dynamic-range 3D measurement. The effectiveness and feasibility of the proposed method are validated through a series of experimental results. Full article

Additive manufacturing (AM) technology has found extensive applications in aerospace, medical, and automotive fields. Defect detection technology remains a research focus in AM process monitoring. While machine learning and neural network algorithms have recently achieved significant advancements in innovative applications for AM defect detection, practical implementations still face challenges, including insufficient detection accuracy and poor system robustness. To address these limitations, this study proposes the YOLOv5-CAD defect detection model. Firstly, the convolutional block attention module (CBAM) is introduced into the core feature extraction module C3 of the backbone network to enhance attention to critical information and improve multi-scale defect target adaptability. Secondly, the original CIoU loss function is replaced with the Alpha-IoU loss function to accelerate network convergence and strengthen system robustness. Additionally, a fully decoupled detection head substitutes the original coupled head in the YOLOv5s model, separating the object classification and bounding box regression tasks to improve detection accuracy. Finally, a polarization technology-based visual monitoring system is developed to acquire defect images of laser AM workpieces, establishing the model’s training sample database. Compared with YOLOv5, the proposed model demonstrates a 2.5% improvement in precision (P), 2.2% enhancement in recall (R), 3.1% increase in mean average precision (mAP50), and 3.2% elevation in mAP50-95. These quantitative improvements confirm the model’s capability to provide robust and real-time technical solutions for industrial AM quality monitoring, effectively addressing current limitations in defect detection accuracy and system reliability. Full article

Phase measuring deflectometry (PMD) plays a more and more significant role in the measurement of specular surfaces. However, most of the deflectometric methods are only suitable for continuous specular surfaces, but not for the discontinuous surfaces. In this work, with the hardware of stereoscopic PMD, a mechanism is introduced so that a specular surface can be reconstructed iteratively with the pre-known coordinate of a reflecting point. Based on the mechanism and the excellent local properties of the B-spline surface, a reconstruction method suitable for both kinds of specular surfaces is proposed. Meanwhile, to resist the noise of the single point, this work mathematically analyzes the mechanism of the method. With the mathematical conclusion, the sparse point cloud solved using stereoscopic PMD is employed to scale the B-spline surfaces, improving the accuracy of reconstruction. Simulated and actual experiments are carried out, and the results show high accuracy and robustness of the PMD system and the reconstruction method. Full article

Millimeter-wave (MMW) radar is capable of providing high temporal–spatial measurements of the ocean surface. Some topics, such as the characterization of the radar echo, have attracted widespread attention from researchers. However, most existing research studies focus on the backscatter of the ocean surface at low microwave bands, while the sea surface backscattering mechanism in the 77 GHz frequency band remains not well interpreted. To address this issue, in this paper, the investigation of the scattering mechanism is carried out for the 77 GHz frequency band ocean surface at small incidence angles. The backscattering coefficient is first simulated by applying the quasi-specular scattering model and the corrected scattering model of geometric optics (GO4), using two different ocean wave spectrum models (the Hwang spectrum and the Kudryavtsev spectrum). Then, the dependence of the sea surface normalized radar cross section (NRCS) on incidence angles, azimuth angles, and sea states are investigated. Finally, by comparison between model simulations and the radar-measured data, the 77 GHz frequency band scattering characterization of sea surfaces at the near-nadir incidence is verified. In addition, experimental results from the wave tank are shown, and the difference in the scattering mechanism is further discussed between water surfaces and oceans. The obtained results seem promising for a better understanding of the ocean surface backscattering mechanism in the MMW frequency band. It provides a new method for fostering the usage of radar technologies for real-time ocean observations. Full article

Three-dimensional (3D) reconstruction of high-dynamic-range (HDR) surfaces plays an important role in the fields of computer vision and image processing. Traditional 3D measurement methods often face the risk of information loss when dealing with surfaces that have HDR characteristics. To address this issue, this paper proposes a simple 3D reconstruction method, which combines the features of non-overexposed regions in polarized and unpolarized images to improve the reconstruction quality of HDR surface objects. The optimum fringe regions are extracted from images with different polarization angles, and the non-overexposed regions in normally captured unpolarized images typically contain complete fringe information and are less affected by specular highlights. The optimal fringe information from different polarized image groups is gradually used to replace the incorrect fringe information in the unpolarized image, resulting in a complete set of fringe data. Experimental results show that the proposed method requires only 24~36 images and simple phase fusion to achieve successful 3D reconstruction. It can effectively mitigate the negative impact of overexposed regions on absolute phase calculation and 3D reconstruction when reconstructing objects with strongly reflective surfaces. Full article