Accuracy Of Depth Measurements Research Articles

Methodology for Performing Bathymetric and Photogrammetric Measurements Using UAV and USV Vehicles in the Coastal Zone

The coastal zone is constantly exposed to marine erosion, rising water levels, waves, tides, sea currents, and debris transport. As a result, there are dynamic changes in the coastal zone topography, which may have negative effects on the aquatic environment and humans. Therefore, in order to monitor the changes in landform taking place in the coastal zone, periodic bathymetric and photogrammetric measurements should be carried out in an appropriate manner. The aim of this review is to develop a methodology for performing bathymetric and photogrammetric measurements using an Unmanned Aerial Vehicle (UAV) and an Unmanned Surface Vehicle (USV) in a coastal zone. This publication shows how topographic and bathymetric monitoring should be carried out in this type of zone in order to obtain high-quality data that will be used to develop a Digital Terrain Model (DTM). The methodology for performing photogrammetric surveys with the use of a drone in the coastal zone should consist of four stages: the selection of a UAV, the development of a photogrammetric flight plan, the determination of the georeferencing method for aerial photos, and the specification as to whether there are meteorological conditions in the studied area that enable the implementation of an aerial mission through the use of a UAV. Alternatively, the methodology for performing bathymetric measurements using a USV in the coastal zone should consist of three stages: the selection of a USV, the development of a hydrographic survey plan, and the determination of the measurement conditions in the studied area and whether they enable measurements to be carried out with the use of a USV. As can be seen, the methodology for performing bathymetric and photogrammetric measurements using UAV and USV vehicles in the coastal zone is a complex process and depends on many interacting factors. The correct conduct of the research will affect the accuracy of the obtained measurement results, the basis of which a DTM of the coastal zone is developed. Due to dynamic changes in the coastal zone topography, it is recommended that bathymetric measurements and photogrammetric measurements with the use of UAV and USV vehicles should be carried out simultaneously on the same day, before or after the vegetation period, to enable the accurate measurement of the shallow waterbody depth.

Detection vehicle lateral offset overestimation on asphalt pavement rut depth measurement accuracy

Detection vehicle lateral offset overestimation on asphalt pavement rut depth measurement accuracy

Enhancing Quality Management of PHC Piles through Improved Driving Formulas: A Comprehensive Review and Analysis

In construction projects, conducting dynamic load tests on all piles proves impractical. Selective testing estimates bearing capacity, while the remaining piles rely on penetration depth for management. This approach, however, faces reliability issues due to varying conditions among piles. Technological advancements, such as non-contact hammers and sensors, have enhanced the accuracy of penetration depth measurements during final driving. Nonetheless, relying solely on penetration depth for construction and quality management remains problematic. This study, therefore, focuses on enhancing the use of driving formulas to improve pile quality management, particularly for the widely used pre-stressed high-strength concrete (PHC) piles. To improve pile quality management, existing driving formulas underwent review and refinement. Utilizing 258 dynamic load test data from various sites, the Hiley, Gates, and Danish formulas underwent validation through statistical analysis and graphical comparison. Enhancements to the Gates formula, achieved through curve fitting with actual data and the application of segment-based coefficients, demonstrated increased accuracy in bearing capacity estimation. These improvements offer a more reliable approach to pile quality management in construction projects.

DETERMINING SHALLOW WATER BATHYMETRY BY STEREO PHOTOGRAMMETRY TECHNIQUE USING WORLDVIEW-2 IMAGERY

Bathymetry in shallow-water areas plays an important role in marine environment management, marine economic resource development, and national defense and security. The echo sounding method is currently highly accurate; however, it has to deal with a variety of challenges as a result of the high cost, terrain influence, and meteorological conditions at sea. As remote sensing technology progresses, estimating bathymetry using satellite imagery will help lower measuring costs and expand monitoring coverage, particularly in shallow water, offshore areas, and difficult-to-access areas. In this article, the authors present a technique for determining bathymetry in shallow water regions by utilizing WorldView-2 stereoscopic images. The accuracy of stereoscopic depth measurement using WorldView-2 images is assessed by field depth measurement using the single-beam echo sounder. The study area is Nam Yet Island. The coefficient of determination (R2) between the two datasets (field measurement data and using satellite images) is 0.9. The stereoscopic satellite image method of depth measurement is highly accurate for regions with depths below 5 meters. The accuracy of the stereoscopic measurement decreases as the depth increases by more than 5 meters.

Long-term Satellite-derived Bathymetry of Arctic Supraglacial Lake from ICESat-2 and Sentinel-2

Abstract. The polar ice sheets serve as natural thermostats, regulating Earth’s temperatures. The Greenland Ice Sheet (GrIS), the second-largest ice sheet, is a critical indicator of climate change and global warming. Estimating the volume of supraglacial lakes on the GrIS, which is directly linked to the extent of melting in the Arctic ice sheet, requires information on both lake area and water depth. Conventional bathymetric methods (i.e., airborne bathymetric LiDAR, shipborne echo-sounder) are commonly used for accurate water depth measurement. However, polar supraglacial lakes face challenging conditions, leading to uncertainties in their spatial and temporal distribution. To overcome the limitations, this study combines Sentinel-2 and ICESat-2 (Ice, Cloud, and Land Elevation Satellite-2) to estimate bathymetry and detect changes in lake volume on the GrIS from 2019 to 2023. Firstly, Sentinel-2 images were pre-processed, and ICESat-2 single-photon LiDAR points were extracted using the DBSCAN (Density-Based Spatial Clustering of Applications with Noise) method, followed by the bathymetric corrections as the training data. Subsequently, three bathymetry models (i.e., log-linear, log-ratio, and BP (Back Propagation) neural network) were constructed using Sentinel-2 images and ICESat-2 data. Lastly, the highresolution ArcticDEM (Arctic Digital Elevation Model) was used as the validation data to assess the satellite-derived bathymetry accuracy. In this study, the log-ratio model yielded the best results with the R2, RMSE, and MAE of 0.92, 0.79 m (lower than 10% of the maximum depth), and 0.62 m. The results demonstrate the feasibility of the integrated active and passive remote sensing approach for bathymetry in Arctic supraglacial lakes.

Experiments with Combining LiDAR and Camera Data Acquired by UAS and Smartphone to Support Mapping

Abstract. Aerial mapping using Unmanned Aerial Systems (UAS), such as the DJI Mavic 3 Enterprise, has revolutionized photogrammetry, enabling efficient data capture for small-scale projects. The typical nadir perspective of UAS mapping, however, imposes limitations on capturing critical details of features due to its predominantly vertical viewpoint. Overcoming this challenge often requires manual, low-altitude flights by experienced UAS pilots to achieve high-angle oblique perspectives, unless gimbled camera mount is used. This study explores the integration of high oblique angle perspectives using the iPhone 15 Pro, which boasts advanced camera capabilities and an integrated LiDAR sensor, to complement UAS imagery. The iPhone 15 Pro's camera sensors provide a Ground Sampled Distance (GSD) comparable to UAS cameras, while its LiDAR sensor, with about five meters of range, enhances mapping capabilities by delivering accurate depth measurements in close range. By utilizing various georeferencing options for the imagery and LiDAR data from the iPhone 15 Pro with UAS nadir imagery, we can achieve a more comprehensive object space reconstruction, significantly improving the accuracy of geospatial mapping. Both the Mavic 3 Enterprise and the iPhone 15 Pro, though operating independently on their respective platforms, support Real-Time Kinematic (RTK) corrections, facilitating precise positioning for the entire system trajectory. Strategic placement and utilization of Ground Control Points (GCPs) aid in the georeferencing of the complete dataset, enhancing its overall accuracy. To validate the accuracy of the acquired data, checkpoints are established on-site. The positions derived from the integrated UAS and iPhone 15 Pro data are compared against these checkpoints to quantify the accuracy and reliability of the data. Additionally, Post-Processed Kinematic (PPK) techniques are employed to validate the trajectories of all data collection systems, ensuring the reliability of the acquired data, especially in instances where RTK corrections may be lacking. In summary, this research showcases comprehensive, multi-dimensional geospatial datasets by conducting validation studies that assess the accuracy and reliability of georeferenced datasets against known ground truth checkpoints. Such validation studies are crucial for identifying gaps in current methodologies and suggesting areas for improvement, thereby aiming to enhance the quality and accuracy of geospatial mapping applications. Through the integration of UAS and smartphone mapping, complemented by rigorous validation efforts, we aspire to achieve improved geospatial mapping accuracy.

Quantitative Evaluation of the Batwing Effect on Depth Measurement in Coherence Scanning Interferometry for Rectangular Gratings

Coherence scanning interferometry (CSI) is a widely used non-contact method for measuring areal surface topography. The calculation of groove depth typically employs the ‘W/3 rule’ specified in ISO 5436-1. However, the batwing effect causes overshoots near the groove edges, which can introduce noise singularities in the selected W/3 region for depth calculation, thereby affecting the measurement of rectangular grating depth. This paper introduces the definition of batwing height and width and proposes a simulation model that considers various factors, such as the center wavelength, spectrum of the illuminating light, shadow effect, and numerical aperture, to analyze their influence on depth measurement. The simulation results demonstrate that the batwing width is the primary factor influencing depth measurement for small grating periods. Specifically, a decrease in numerical aperture increases the batwing width, leading to larger depth measurement errors, while a decrease in the center wavelength reduces the batwing width, resulting in smaller depth measurement errors. The influence of the illumination spectrum and shadow effect on depth measurement is found to be minor. Experimental validation using a step standard consisting of rectangular gratings with different periods and depths confirms the agreement between the experimental and simulated results. The proposed method provides a quantitative evaluation of depth measurement accuracy in CSI.

A Survey of Monocular Depth Estimation based on Deep Learning

Depth information is very important for machines to perceive the environment and estimate their own state. Significant advances in robotics engineering and self-driving cars in recent decades have increased the demand for accurate depth measurements. Traditional depth estimation methods include motion structure and stereo vision matching, but these are based on the feature correspondence of multiple viewpoints, and at the same time, the predicted depth map is sparse. Depth estimation is a traditional task in computer vision that can be properly predicted by applying a variety of procedures, whereas inferring depth information from a single image is an ill-posed problem. The main objective of this paper is to provide a brief overview of the development of monocular depth estimation techniques based on deep learning. This article attempts to give an overview of supervised, unsupervised, and datasets and evaluation metrics. We conclude with a brief analysis of future developments.

Moving event detection from LiDAR point streams

In dynamic environments, robots require instantaneous detection of moving events with microseconds of latency. This task, known as moving event detection, is typically achieved using event cameras. While light detection and ranging (LiDAR) sensors are essential for robots due to their dense and accurate depth measurements, their use in event detection has not been thoroughly explored. Current approaches involve accumulating LiDAR points into frames and detecting object-level motions, resulting in a latency of tens to hundreds of milliseconds. We present a different approach called M-detector, which determines if a point is moving immediately after its arrival, resulting in a point-by-point detection with a latency of just several microseconds. M-detector is designed based on occlusion principles and can be used in different environments with various types of LiDAR sensors. Our experiments demonstrate the effectiveness of M-detector on various datasets and applications, showcasing its superior accuracy, computational efficiency, detection latency, and generalization ability.

A Couch Mounted Smartphone-based Motion Monitoring System for Radiation Therapy

PurposeSurface-guided radiation-therapy (SGRT) systems are being adopted into clinical practice for patient setup and motion monitoring. However, commercial systems remain cost prohibitive to resource-limited clinics around the world. Our aim is to develop and validate a smartphone-based application using LiDAR cameras (such as on recent Apple iOS devices) for facilitating SGRT in low-resource centers. The proposed SGRT application was tested at multiple institutions, and validated using phantoms and volunteers against various commercial systems to demonstrate feasibility. Methods and MaterialsAn iOS application was developed in Xcode and written in Swift using the Augmented-Reality (AR) Kit and implemented on an Apple iPhone 13 Pro with a built-in LiDAR camera. The application contains multiple features: 1) visualization of both the camera and depth video feeds (at a ∼60Hz sample-frequency), 2) region-of-interest (ROI) selection over the patient's anatomy where motion is measured, 3) chart displaying the average motion over time in the ROI, and 4) saving/exporting the motion traces and surface map over the ROI for further analysis. The iOS application was tested to evaluate depth measurement accuracy for: 1) different angled surfaces, 2) different field-of-views over different distances, and 3) similarity to a commercially available SGRT systems (Vision RT AlignRT® and Varian IDENTIFY™) with motion phantoms and healthy volunteers across three institutions. Measurements were analyzed using linear-regressions and Bland-Altman analysis. ResultsCompared to the clinical system measurements (reference), the iOS application showed excellent agreement for depth (r=1.000,p<0.0001; bias=-0.07±0.24cm) and angle (r=1.000,p<0.0001; bias=0.02±0.69°) measurements. For free-breathing traces, the iOS application was significantly correlated to phantom motion (institute 1: r=0.99,p<0.0001; bias=-0.003±0.03cm; institute 2: r=0.98,p<0.0001; bias=-0.001±0.10cm; institute 3: r=0.97,p<0.0001; bias=0.04±0.06cm) and healthy volunteer motion (institute 1: r=0.98,p<0.0001; bias=-0.008±0.06cm; institute 2: r=0.99,p<0.0001; bias=-0.007±0.12cm; institute 3: r=0.99,p<0.0001; bias=-0.001±0.04cm). ConclusionThe proposed approach using a smartphone-based application provides a low-cost platform that could improve access to surface-guided radiation therapy accounting for motion.

Polarization structured light 3D depth image sensor for scenes with reflective surfaces

Highly reflective surfaces are notorious in the field of depth sensing and three-dimensional (3D) imaging because they can cause severe errors in perception of the depth. Despite recent progress in addressing this challenge, there are still no robust and error-free solutions. Here, we devise a polarization structured light 3D sensor for solving these problems, in which high-contrast-grating (HCG) vertical-cavity surface-emitting lasers (VCSELs) are used to exploit the polarization property. We demonstrate accurate depth measurements of the reflective surfaces and objects behind them in various imaging situations. In addition, the absolute error and effective measurement range are measured to prove the applicability for a wide range of 3D applications. Our work innovatively combines polarization and depth information, opening the way for fully understanding and applying polarization properties in the 3D domain.

Advanced Ultrasonic Inspection of Thick-Section Composite Structures for In-Field Asset Maintenance.

An investigation into the inspection capabilities of in-field advanced ultrasound detection for use on ultra-thick (20 to 100 mm) glass fibre-reinforced polyester composites is presented. Plates were manufactured using custom moulding techniques, such that delamination flaws were created at calibrated depths. The full matrix capture technique with an on-board total focussing method was used to detect flaws scanned by a 0.5 MHz linear array probe. Flaw through-thickness dimensions were altered to assess the threshold for crack face separation at which delaminations could be identified. Furthermore, part thickness and in-plane flaw dimensions were varied to identify the inspection capability limitations of advanced ultrasonics for thick composites. The results presented in this study demonstrate an inverse relationship between the ability to find delaminations and plate thicknesses, with inspections successful at depths up to 74 mm. When the delamination thickness exhibited surface-to-surface contact, the inspection capability was reduced to 35 mm. There was an exponential decay relationship between the accuracy of the flaw depth measurement and plate thickness, likely due to the necessity of low probe frequencies. The effective inspection depth was determined to be in the range of 1 to 20 times the wavelength. It is speculated that the accuracy of measurements could be improved using probes with novel coupling solutions, and detectors with optimised signal processing/filtration algorithms.

Validation Technique for Heavy Vehicle Simulator Rut Measurement with Florida Laser Rut Meter

Abstract The performance of a pavement system is typically measured in structural and functional terms. Agencies often prioritize minimizing rutting as it indicates the mixture and structural inadequacy and can lead to safety concerns such as hydroplaning. Therefore, accurate rut depth measurement is critical to pavement management as well as to assess the performance of materials designed to resist rutting. To that measure, Florida Department of Transportation (FDOT) uses two heavy vehicle simulators (HVSs) as part of an accelerated pavement testing (APT) program to advance pavement research and implement new pavement materials, designs, and construction methods. Each HVS device is equipped with an on-board laser profiling system for rut depth measurement. To ensure accurate and reproducible rut depth measurements, a laser-based reference device was developed and constructed by FDOT to verify rut depth measurements made by both HVS devices during APT research studies. This article describes the rut depth reference device, the verification process, and data analysis methods used to validate the accuracy of the HVS devices.

Accurate Measurement of Frozen Soil Depth Based on I-TDR

In this study, a new method for determining the depth of frozen soil, Impulse Response Time Domain Reflectometry, is discussed. This method uses the principle of impedance measurement and the law of time–frequency domain convolution to convert the frequency-domain reflection signal into a time-domain signal and accurately determines the soil freezing front by measuring the difference between the impedance of frozen soil and unfrozen soil. The advantage of this method is that it solves the problems of small bandwidth, long rising edge time, and large measurement errors in the traditional TDR method to effectively improve the measurement accuracy of the soil-freezing front. Under laboratory conditions, soils of different textures (sand, loess, black soil, and red soil) were selected for experimental determination, and the results showed that compared with the traditional TDR method, the RMSE of the I-TDR method was small, and the method was applicable under different soil texture conditions, which could provide a new method for monitoring frozen soil in cold areas. In addition, the application of this method has important guiding significance for improving the efficiency of winter irrigation water, especially for guiding agricultural production, farmland irrigation, drainage engineering construction, meteorological frozen soil monitoring, and other aspects in cold and arid areas.

Accurate and Rapid Measurement of Soil Dry Depth Using Ultrasonic Reflection Waves

Soil dry depth is a key parameter that determines soil fertility and nutrient availability, ultimately affecting crop yield and quality. However, accurately measuring the dry depth of soil has been a challenge. In this work, we propose using reflective ultrasonic waves to measure dry depth in soil. Four soil types, including clay, sandy loam, silty loam, and sandy were prepared and the feasibility of the method was demonstrated through theoretical analysis. An experimental measurement system was established to verify the consistency between ultrasonic measurements and manually measured values. Two statistics were used in Ordinary Least Squares (OLS) regression to evaluate the model fit: R-square (R2) and Root mean square error (RMSE). The results indicate that the proposed method provides a higher accuracy in estimating the dry depth of sandy loam and silty loam (R2 values of 0.9899 and 0.992 for sandy loam, RMSE values of 1.57% and 1.5% for silty loam) than those of the clay and sandy samples (R2 values of 0.9896 and 0.9874 for clay, RMSE values of 1.66% and 1.77% for sandy). The maximum measurement errors for all the soil type predictions are below 6%; the overall accuracy was acceptable. Our findings suggest that ultrasonic measurement is an efficient and cost-effective approach for measuring soil dry depth, which could enable the precise control of irrigation water usage and the conservation of valuable water resources.

Integrated high precision time-of-flight depth sensing and optical communication based on Hamiltonian coding

Convergence of sensing and communication in the same architecture is being considered for implementation in the future sixth-generation (6G) networks due to spectrum congestion and cost efficiency. This paper presents an integrated architecture for time-of-flight (ToF) depth sensing and optical communication simultaneously based on Hamiltonian coding function. The feasibility and performance of this framework are verified by theoretical simulation and experiments. Compared with sinusoid coding scheme, the proposed system based on single-frequency Hamiltonian coding modulation achieves a depth measurement accuracy of tens of millimeters and zero error code in communication, and the data rate 20 Mbps. By developing a multi-frequency Hamiltonian coding fusion algorithm, not only the maximum unambiguous range is increased from 15 m to 60 m, but the average ranging precision is improved to 4.46 mm as well. Experimentally, information of 1000 bits carried by single-frequency is demonstrated. Multi-frequency fusion system can accommodate three times that of single frequency. Within the maximum measurable experimental scope, the value of communication bit error rate (BER) always tends to be zero, under 7% pre-forward error correction (pre-FEC) limit of 3.8×10−3.

Non-integral depth measurement of high-aspect-ratio multi-layer microstructures using numerical-aperture shaped beams

This work presents a new optical metrology technique for accurate depth measurement of individual high-aspect-ratio (HAR) multi-layer microstructures with high spatial resolution and signal-to-noise ratio (SNR). To resolve the limitations of existing optical metrology techniques, light shaping with high light efficiency is optimized using numerical-aperture controlled laser beams. Experimental tests evidenced a 28-fold improvement in SNR for measuring large-depth structures when compared with measurements obtained using traditional broadband incoherent illumination. More importantly, instead of integral measurement characteristics currently limited by conventional spectral reflectometry or scatterometry, non-integral depth measurement of an individual microstructure from densely spaced microstructures such as through-silicon vias (TSV) and redistribution layers (RDL) can be realized using the developed optical measuring system with desired numerical aperture and field of view achieved simultaneously. As demonstrated by the measurement of a single submicron structure with linewidth as small as 0.6 µm and an aspect ratio of 5, the precision of depth measurement can be kept within a few nanometers.

Automated pothole condition assessment in pavement using photogrammetry-assisted convolutional neural network

ABSTRACT Automated detection of pavement distress can prevent deterioration of premature surface disintegration in pavements. Potholes that are a common sight in harsh and cold terrains are a severe threat to road safety and a major contributing factor to pavement distress. To facilitate timely detection and repair of potholes, a computationally light and feasible, intelligent pavement pothole detection system is proposed by developing a novel workflow for image-based detection and severity assessment. A single-stage CNN architecture, RetinaNet is modified and optimised to best detect potholes and used in combination with a novel pothole depth estimation algorithm. A comparative evaluation of the model’s performance against the existing state-of-the-art model on the benchmark dataset establishes the proposed model’s high performance and applicability in real-time scenarios. The depth estimation algorithm is based on a 3D road surface model generated by employing the photogrammetric process of structure from motion (SfM). The point cloud data obtained thereafter, is used for accurate measurement of pothole depth. The comparison of the derived depth with the onsite depth measurement of the pothole reveals a mean error below 5%. This method leads to a practical and intelligent solution to be implemented as part of a potential pavement health assessment system for future practice.

High-Quality 3D Visualization System for Light-Field Microscopy with Fine-Scale Shape Measurement through Accurate 3D Surface Data.

We propose a light-field microscopy display system that provides improved image quality and realistic three-dimensional (3D) measurement information. Our approach acquires both high-resolution two-dimensional (2D) and light-field images of the specimen sequentially. We put forward a matting Laplacian-based depth estimation algorithm to obtain nearly realistic 3D surface data, allowing the calculation of depth data, which is relatively close to the actual surface, and measurement information from the light-field images of specimens. High-reliability area data of the focus measure map and spatial affinity information of the matting Laplacian are used to estimate nearly realistic depths. This process represents a reference value for the light-field microscopy depth range that was not previously available. A 3D model is regenerated by combining the depth data and the high-resolution 2D image. The element image array is rendered through a simplified direction-reversal calculation method, which depends on user interaction from the 3D model and is displayed on the 3D display device. We confirm that the proposed system increases the accuracy of depth estimation and measurement and improves the quality of visualization and 3D display images.

Probabilistic instance shape reconstruction with sparse LiDAR for monocular 3D object detection

Monocular 3D object detection aims to localize objects in 3D space from a single image. This is a difficult problem due to the lack of accurate depth measurements. Many methods predict depths upfront using a pre-trained vision-based depth estimator to assist in 3D object detection. However, the methods show limited improved performances because of the depth inaccuracy and the neglect of depth confidence. In this paper, we propose a new end-to-end 3D object detection framework by combining a monocular camera with a cheap 4-beam LiDAR. The minimal LiDAR signal is leveraged as an additional input to predict high-quality and dense depth maps from monocular images. Meanwhile, several 3D proposals are generated by a keypoint-based detector. The key challenge is encoding the depth confidence to capture the depth estimation uncertainty. Therefore, we propose probabilistic instance shape reconstruction to exploit instance shape information for box refinement. Our method employs a fully differentiable end-to-end framework, making it simple and efficient. The experimental results on the KITTI dataset demonstrate that the proposed method achieves state-of-the-art performance, thus validating the effectiveness of the sparse LiDAR data and the probabilistic instance shape reconstruction. Code is available at https://github.com/xjtuwh/SparseLiDAR_fusion.