Accurate Depth Research Articles

Study on Fatigue Performance and Rutting Prediction Model of Steel Slag Asphalt Mixture

Steel slag, a significant industrial waste, poses environmental and land issues due to its accumulation. This research explores using steel slag to replace natural aggregates in steel slag asphalt mixture (SSAM) at varying levels by weight: 25%, 50%, 75%, and 100%. The research evaluated the pavement and fatigue performance of SSAM, noting that dynamic stability (DS) peaked at a 75% slag content. Although low-temperature flexibility decreased, it remained within acceptable limits. The mixture met or exceeded Chinese specifications for resistance to water and traffic stress. Fatigue life varied with slag content, decreasing at high levels due to increased stiffness and stress. A rutting prediction model for SSAM was developed, utilizing a back propagation neural network for accurate rut depth forecasts, demonstrating the model's precision with a fit of 0.99744. This study suggests optimized slag content enhances SSAM performance, offering a sustainable approach to managing steel slag.

Depth range extended digital holography for precise 3D profile imaging via dual frequency interval sweeping

A 3D profile imaging method is proposed by using digital holography swept at dual frequency intervals. The dual frequency interval sweeping is achieved by altering the frequency of the tunable laser with two selected frequency intervals. An infrared camera was used to capture the interferometric patterns during two sweeping periods, and depth profile was reconstructed from multiple digital holograms. Phase image across the target region is extracted at each frequency and the phase at each pixel is varied as the frequencies are swept at a prefixed interval in each period. From measured phases, the congruence equations are constructed to achieve the 3D profile of the target object. The proposed sweeping method extends the measurement range by about ten times compared to the classical single frequency interval sweeping method, with a high depth accuracy of within 20 μm over a range of 4 cm. The proposed method is verified by both numerical simulation and experiment. Automatic extraction of reconstructed distances is achieved to facilitate automated 3D profile imaging.

Analysis of corneal remodeling post-myopic photorefractive keratectomy with the WaveLight® EX500 excimer laser

Refractive error is becoming a significant public health issue. Photorefractive Keratectomy (PRK) is a corneal surface surgical technique that removes the corneal epithelium before stromal photoablation by ultraviolet radiation from the Excimer laser. We designed a retrospective study to characterize corneal remodeling after myopic Photorefractive Keratectomy and assess the accuracy of laser-predicted ablation depth (AD). This study took place in 15–20 National Ophthalmology Hospital, Paris, France. 150 eyes with preoperative manifest spherical equivalent between − 10.00D and − 0.25D and cylinder < 3D, treated with the WaveLight® EX500 laser between 01/2019 and 01/2023, were followed for at least three months. The main outcome measurements were postoperative changes in epithelial (ET) and stromal (ST) thicknesses measured with spectral domain optical coherence tomography and mean simulated keratometry (SimK) assessed with corneal topography. The central ET significantly decreased at M1, increased over the preoperative value from M1 to M6, and stabilized after M6. The increase in central ET after M1 was associated with an increase in mean SimK (r = 0.34). The achieved AD was 7.9 ± 8.0 µm greater than the laser-predicted AD. Stromal over-ablation was significantly and independently associated with myopia > 6D preoperative mean SimK > 44D and transepithelial procedures.

Quantitative detection of the width and depth of subsurface defects in curved structures using ultrasonic Rayleigh waves

ABSTRACT Rayleigh waves are highly sensitive to surface and subsurface defects. However, the varying surface profiles of complex structures induce constant changes in the Rayleigh waveform. Furthermore, Rayleigh waves encompass multi-dimensional characteristics of subsurface defects, significantly increasing the difficulty of quantitatively detecting the sizes of subsurface defects in different dimensions. This paper presents a machine learning (ML)-based method for quantitatively detecting subsurface defect depth and width in curved structures using laser-generated Rayleigh waves. An experimentally validated finite element model is first established to construct a dataset of Rayleigh wave signals labeled with multidimensional subsurface defect sizes. The dataset is augmented from 188 to 368 samples using the Mix-up algorithm, with additional experimental signals incorporated. Feature parameters of Rayleigh waves are extracted in both time and frequency domains through waveform analysis and sliding window wavelet transform (SWT). These features are used to train ML models: Gaussian Process (GP), Extreme Gradient Boosting (XGBoost), and Random Forest (RF). Comparative results demonstrate GP achieves the highest prediction accuracy for subsurface defect depth and width on the test set, reaching 98.64% and 97.73%, respectively. The study highlights the integration of experimental and simulated data in training, which reduces costs while enhancing model robustness in practical applications.

Integrated UAV and Satellite Multi-Spectral for Agricultural Drought Monitoring of Winter Wheat in the Seedling Stage.

Agricultural droughts are a threat to local economies, as they disrupt crops. The monitoring of agricultural droughts is of practical significance for mitigating loss. Even though satellite data have been extensively used in agricultural studies, realizing wide-range, high-resolution, and high-precision agricultural drought monitoring is still difficult. This study combined the high spatial resolution of unmanned aerial vehicle (UAV) remote sensing with the wide-range monitoring capability of Landsat-8 and employed the local average method for upscaling to match the remote sensing images of the UAVs with satellite images. Based on the measured ground data, this study employed two machine learning algorithms, namely, random forest (RF) and eXtreme Gradient Boosting (XGBoost1.5.1), to establish the inversion models for the relative soil moisture. The results showed that the XGBoost model achieved a higher accuracy for different soil depths. For a soil depth of 0-20 cm, the XGBoost model achieved the optimal result (R2 = 0.6863; root mean square error (RMSE) = 3.882%). Compared with the corresponding model for soil depth before the upscaling correction, the UAV correction can significantly improve the inversion accuracy of the relative soil moisture according to satellite remote sensing. To conclude, a map of the agricultural drought grade of winter wheat in the Huaibei Plain in China was drawn up.

NeRF-based Polarimetric Multi-view Stereo

In this paper, we introduce NeRF-based Polarimetric Multi-view Stereo (NPMVS), a novel 3D reconstruction method that combines the advantages of neural radiance field (NeRF) and shape-from-polarization (SfP) to address the challenge posed by textureless areas while preserving the fine-scale geometric details. Our method first leverages neural rendering to yield depth priors for each input view, subsequently estimates more accurate depths and normals using polarimetric refinement. We further introduce a pixel-wise depth rectification process to address the scaling problem inherent to the polarimetric refinement procedure. In addition, we contribute a new realistic pBRDF-based multi-view synthetic dataset, comprised of RGB and polarization images rendered under real-world lighting conditions, which will serve as a valuable resource for future research in this field. Experimental evaluations on both synthetic and real-world datasets validate the superiority of NPMVS, demonstrating its advantage over other state-of-the-art multi-view stereo and shape-from-polarization methods.

Estimation of top depth to underground targets of Karous-Hjelt and Fraser filtering of VLF-EM measurements: The Thumb's rule approach

Prediction of the exact location and depth of underground targets with the very low-frequency electromagnetic (VLF-EM) technique is one of the most important and difficult tasks in geophysical investigations. This study examined and compared the conventional 2D KIFFILT inversion pseudo-section and the use of Thumb's rule technique in the Fraser filter plot to estimate the top depth of underground targets. The VLF-EM measurement was performed over several empirical buried target models to identify anomalies or geophysical responses corresponding to subsurface targets. The Karous-Hjelt and Fraser filtering techniques were applied to estimate the depth of the identified anomalies using Thumb's rule and the conventional 2D KIFFILT inversion. The signal behaviours of the VLF-EM current density pseudo-sections and the application of Thumb's rule effectiveness in delineating empirical buried target models were examined. Thumb's rule shows 65 % accuracy with the actual depth of the empirical buried target models, while the conventional 2D KHFFILT inversion shows 30 % accuracy with the actual depth of burial. Thumb's rule is more effective and precise in predicting the accurate depth of underground targets. The influences of conductive and resistive materials on VLF-EM signals and the challenges of VLF-EM surveys were discussed. Thumb's rule is suggested as a substantial technique for estimating top depth to the underground target where depth estimation is of prime interest due to its large degree of accuracy. In addition, the total depth of the current density distribution was noted to be increased when the distance between measuring points increased. This means that VLF-EM signals with longer wavelengths indicate deeper depth penetration into the ground than signals with shorter wavelengths. The accuracy of Thumb's rule regarding top depth estimation of the anomalies has been successfully tested and validated, which can be used for VLF-EM investigation where accurate depth estimation is required. The VLF-EM technique can be considered reliable for depth estimation using Thumb's rule approach, which applies to a wide range of subsurface investigations.

CAFNet: Context aligned fusion for depth completion

Depth completion aims at reconstructing a dense depth from sparse depth input, frequently using color images as guidance. The sparse depth map lacks sufficient contexts for reconstructing focal contexts such as the shape of objects. The RGB images contain redundant contexts including details useless for reconstruction, which reduces the efficiency of focal context extraction. The unaligned contextual information from these two modalities poses a challenge to focal context extraction and further fusion, as well as the accuracy of depth completion. To optimize the utilization of multimodal contextual information, we explore a novel framework: Context Aligned Fusion Network (CAFNet). CAFNet comprises two stages: the context-aligned stage and the full-scale stage. In the context-aligned stage, CAFNet downsamples input RGB-D pairs to the scale, at which multimodal contextual information is adequately aligned for feature extraction in two encoders and fusion in CF modules. In the full-scale stage, feature maps with fused multimodal context from the previous stage are upsampled to the original scale and subsequentially fused with full-scale depth features by the GF module utilizing a dynamic masked fusion strategy. Ultimately, accurate dense depth maps are reconstructed, leveraging the GF module’s resultant features. Experiments conducted on indoor and outdoor benchmark datasets show that the CAFNet produces results comparable to state-of-the-art methods while effectively reducing computational costs.

A Self-Supervised Network-Based Smoke Removal and Depth Estimation for Monocular Endoscopic Videos.

In minimally invasive surgery videos, label-free monocular laparoscopic depth estimation is challenging due to smoke. For this reason, we propose a self-supervised collaborative network-based depth estimation method with smoke-removal for monocular endoscopic video, which is decomposed into two steps of smoke-removal and depth estimation. In the first step, we develop a de-endoscopic smoke for cyclic GAN (DS-cGAN) to mitigate the smoke components at different concentrations. The designed generator network comprises sharpened guide encoding module (SGEM), residual dense bottleneck module (RDBM) and refined upsampling convolution module (RUCM), which restores more detailed organ edges and tissue structures. In the second step, high resolution residual U-Net (HRR-UNet) consisting of a DepthNet and two PoseNets is designed to improve the depth estimation accuracy, and adjacent frames are used for camera self-motion estimation. In particular, the proposed method requires neither manual labeling nor patient computed tomography scans during the training and inference phases. Experimental studies on the laparoscopic data set of the Hamlyn Centre show that our method can effectively achieve accurate depth information after net smoking in real surgical scenes while preserving the blood vessels, contours and textures of the surgical site. The experimental results demonstrate that the proposed method outperforms existing state-of-the-art methods in effectiveness and achieves a frame rate of 94.45fps in real time, making it a promising clinical application.

Application of gradient boosting machine in satellite-derived bathymetry using Sentinel-2 data for accurate water depth estimation in coastal environments

Accurate water depth estimation is crucial in coastal environmental management, resource exploration, and ecological protection. Traditional water depth measurement methods are often time-consuming and costly, especially in vast sea areas where their application is limited. However, with the rapid development of remote sensing technology, particularly the widespread use of high-resolution satellite imagery, water depth remote sensing has emerged as a more efficient, economical, and widely applicable solution. In this study, we utilized Sentinel-2 satellite data and applied various algorithms to accurately estimate water depth in the Nanshan Port area. The results showed that the Gradient Boosting Machine (GBM) model excelled in monitoring shallow water and coastal environments, effectively addressing challenges such as light attenuation and water scattering in turbid waters. Compared to traditional methods, GBM-generated predictions were smoother and more detailed. This study not only demonstrates the significant potential of satellite remote sensing for water depth measurement but also points to future directions for algorithm optimization and the integration of remote sensing technologies. It is expected to bring revolutionary progress to oceanic scientific research and coastal management.

Microstructuring of fused silica by laser-induced microplasma using a pyrographite target for phase optical elements fabrication

In this paper, we purpose to use laser-induced microplasma occurred from a pyrographite target by laser ablation for microstruturing of fused silica plate to fabricate phase optical elements. Laser parameters for erosive plasma torch formation and subsequent etching of fused silica were determined based on preliminary experimental studies and estimated energy calculations. The influence of the overlap coefficients in the focal spot on the reproducibility of etching depths results and the deviation accuracy of etching depths with the found laser parameters was experimentally studied. The best results in terms of accuracy (no more than 0.1 μm) in deviations of etching depths are achieved with the overlap coefficient of 0.5. Moreover, the paper presents estimated calculations of the speed and efficiency of ablation, as well as the coefficient of energy conversion from laser radiation to the ablation energy of fused silica, which was ∼ 0.25. Based on achieved results an attempt was made to fabricate the 6-sector SPP for the wavelength of 355 nm.

LightDepth: A resource efficient depth estimation approach for dealing with ground truth sparsity via curriculum learning

Accurate depth estimation from monocular images is critical for various applications such as robotics, augmented reality, and autonomous navigation. However, achieving high accuracy while maintaining computational efficiency is a major challenge, particularly for resource-constrained devices. In this paper, we present LightDepth, an approach that leverages curriculum learning to estimate depth efficiently while taking into account resource constraints. It modifies the ground truth sparse depth maps from the KITTI dataset by resizing them to 31 extents during training to reduce sparsity and control complexity. The resulting model achieves comparable accuracy to state-of-the-art large models while outperforming them in response time by 71%. Our approach outperforms resource-efficient models regarding depth accuracy (measured by RMSE), achieving a 56% improvement. LightDepth is designed to be fast and resource-efficient, making it suitable for deployment in resource-constrained devices. It also balances the trade-off between accuracy and resource efficiency. All codes are available online at https://github.com/fatemehkarimii/lightdepth.

Depth estimation from monocular endoscopy using simulation and image transfer approach

Obtaining accurate distance or depth information in endoscopy is crucial for the effective utilization of navigation systems. However, due to space constraints, incorporating depth cameras into endoscopic systems is often impractical. Our goal is to estimate depth images directly from endoscopic images using deep learning. This study presents a three-step methodology for training a depth-estimation network model. Initially, simulated endoscopy images and corresponding depth maps are generated using Unity based on a colon surface model obtained from segmented computed tomography colonography data. Subsequently, a cycle generative adversarial network model is employed to enhance the realism of the simulated endoscopy images. Finally, a deep learning model is trained using the synthesized endoscopy images and depth maps to estimate depths accurately. The performance of the proposed approach is evaluated and compared against prior studies utilizing unsupervised training methods. The results demonstrate the superior precision of the proposed technique in estimating depth images within endoscopy. The proposed depth estimation method holds promise for advancing the field by enabling enhanced navigation, improved lesion marking capabilities, and ultimately leading to better clinical outcomes.

Depth-resolved imaging through scattering media based on modified phasor field diffraction

The imaging through scattering media has significant applications in fields, including rescue and security. However, the phasor field algorithm ignores the effect of the additional optical path introduced by the scattering medium for the imaging of the hidden objects behind scattering media. To improve the accuracy, a modified phasor field diffraction method combined with the spatial average path for depth-resolved imaging through scattering media is proposed here. The additional optical path introduced by the scattering medium is resolved through the spatial average path, while the phasor field describes the free-space propagation of the light in air. We verify the feasibility and robustness of the proposed method on public transient data, real scenes and synthetic data. The results demonstrate that the higher-quality results with the proposed method are obtained, and the accurate depth information of objects is provided compared with the phasor field, as evidenced by evaluating the mean depth. This method has potential applications in medical imaging, remote sensing and other fields.

Learning deformable hypothesis sampling for patchmatch multi-view stereo in the wild

The learnable PatchMatch formulations have recently made progress in Multi-View Stereo (MVS). However, their performance often degrades under complex wild scenes. In this study, we observe that the degradation is mainly caused by the noisy depth hypothesis sampling during iterations of PatchMatch MVS: (i) Within a single iteration, the method mixes all information of a fixed-shape region in the spatial dimension, which introduces sampling noise, especially in regions with abrupt depth changes; (ii) Between iterations, the depth candidate hypotheses are sampled indiscriminately in the depth dimension, which fails to filter out noise in the sampling range and propagates errors to the next iteration. Accordingly, we propose a deformable sampling learning strategy (DefLearn) guided by matching uncertainty to determine the hypothesis set from two perspectives: (i) Encoding the matching uncertainty within the image allows the search region of sampling to fit the depth distribution in the spatial dimension; (i) Modeling the cross-image matching uncertainty more accurately based on the obtained valid hypotheses to capture scene structure information for fine-grained hypothesis searching in the depth dimension. With DefLearn, we develop a deep probabilistic PatchMatch MVS network (PPMNet) for accurate depth estimation with a high awareness of diverse scene structures. Extensive experiments conducted on three challenging datasets demonstrate that our PPMNet significantly outperforms state-of-the-art methods in terms of accuracy and generalization ability, even in particularly challenging wild scenes. Code is available at https://github.com/Geo-Tell/PPMNet.

Model for dimensioning borehole heat exchanger applied to mixed-integer-linear-problem (MILP) energy system optimization

This paper introduces three novel approaches to size geothermal energy piles in a MILP, offering fresh perspectives and potential solutions. The research overlooks MILP models that incorporate the sizing of a geothermal borefield. Therefore, this paper presents a new model utilizing a g-function model to regulate the power limits. Geothermal energy is an essential renewable source, particularly for heating and cooling. Complex energy systems, with their diverse sources of heating and cooling and intricate interactions, are crucial for a climate-neutral energy system. This work significantly contributes to the integration of geothermal energy as a vital energy source into the modelling of such complex systems. Borehole heat exchangers help generate heat in low-temperature energy systems. However, optimizing these exchangers using mixed-integer-linear programming (MILP), which only allows for linear equations, is complex. The current research only uses R-C, reservoir, or g-function models for pre-sized borefields. As a result, borehole heat exchangers are often represented by linear factors such as 50 W/m for extraction or injection limits. A breakthrough in the accuracy of borehole heat exchanger sizing has been achieved with the development of a new model, which has been rigorously compared to two simpler models. The geothermal system was configured for three energy systems with varying ground and bore field parameters. The results were then compared with existing geothermal system tools. The new model provides more accurate depth sizing with an error of less than 5 % compared to simpler models with an error higher than 50 %, although it requires more calculation time. The new model can lead to more accurate borefield sizing in MILP applications to optimize energy systems. This new model is especially beneficial for large-scale projects that are highly dependent on borefield size.

End-to-end stereo matching network with two-stage partition filtering for full-resolution depth estimation and precise localization of kiwifruit for robotic harvesting

Full-resolution depth estimation within operational space of robotic arms and accurate localization of kiwifruits is very important for automated harvesting. Depth estimation is expected to be accurate and full-resolution while current depth estimation methods are susceptible to depth missing due to occlusion and uneven illumination. And depth estimation mostly focuses on fruit localization, while obstacles such as branches and wires, which can affect harvesting strategy, have not been considered. This paper localized kiwifruits based on bounding boxes output by YOLOv8m and full-resolution depth from an end-to-end stereo matching network, i.e., LaC-Gwc Net, which was trained after generating a stereo matching dataset by proposing a two-stage partition filtering algorithm. Results showed that LaC-Gwc Net achieved an end-point error (EPE) of 3.8 pixels, which means that accurate depth estimation can also be achieved for thin obstacles such as the branches and the wires. Additionally, YOLOv8m obtained acceptable results in detecting kiwifruits and their calyxes, reaching mean average precision (mAP) of 93.1% and detection speed of 7.0 ms. The methodology obtained only kiwifruit localization error of 4.0 mm on the Z-axis, which meets requirements of robotic harvesting. Furthermore, this study considered the localization of obstacles in kiwifruit orchards, providing high-precision full-resolution depth estimation for agricultural harvesting robots.

A Deep Learning Estimation for Probing Depth of Transient Electromagnetic Observation

The probing depth of the transient electromagnetic method (TEM) refers to the depth range at which the underground conductivity changes can be effectively detected. It typically ranges from tens of meters to several kilometers and is influenced by factors such as instrument parameters and the conductivity of the subsurface structure. Rapid and accurate probing depth is useful for the selection of appropriate inversion parameters and improving survey accuracy. However, mainstream methods suffer from issues such as low computational precision, large uncertainties, or high computational requirements, making them unsuitable for processing massive airborne electromagnetic data. In this study, we propose a prediction model based on deep learning that can directly compute the probing depth from the TEM responses, and its effectiveness and accuracy are validated through synthetic models and field measurements. We compared the performance of classic deep learning models, including CNN, RESNET, and RNN, and found that RNN performed the best overall on both synthetic and field data. Furthermore, we apply this algorithm to deep learning-based ATEM inversion by constraining the one-dimensional resistivity model depths in the training set, to reduce the non-uniqueness of the inversion, accelerate the convergence, and improve its prediction accuracy.

Auditory perception based milling posture detection and depth control enhancement for orthopedic robots

The use of robots for bone milling is thought to improve surgical efficiency and reduce surgeon fatigue. Surgeons can use their hearing to perceive the milling state and adjust the position of surgical tools. Inspired by this, this paper proposes an automatic tool position control method based on acoustic signal feedback. Firstly, the mapping model between milling depth and sound energy was calibrated through experiments, and the influence of different angles on depth estimation accuracy was tested. Then, a network of CNN-GRU with an attention mechanism is designed to realize milling angle recognition using acoustic features as inputs. Finally, a milling depth automatic control framework with angle optimization was designed and validated through relevant experiments. The results of the experiment showed that the depth estimation error was larger when the milling angle of ball end milling tools (BMT) was within 70-90°. After adding the angle adjustment function, the milling depth accuracy in angle interval 1 (80-90°) and angle interval 2 (70°-80°) increased by about 30% and 10%, respectively.

Superalloys fracture process inference based on overlap analysis of 3D models

Superalloy materials exhibit susceptibility to fracture failures stemming from the influence of thermomechanical factors. To comprehensively understand the fracture mechanisms, material properties, root causes of failure, and the subsequent optimization of alloys, a detailed analysis of the internal fracture process and the morphological traits of the fracture surface is imperative. Traditional analysis of fracture surfaces solely relies on 2D images, thus lacking crucial 3D information. Although in situ experiments can capture the fracture process, their effectiveness is confined to the specimen’s surface, precluding insight into internal changes. Here we introduce an integrated framework encompassing the process of 3D reconstruction of fracture surfaces, aiming to enhance the visual information obtained with micron-level accuracy, visual intuitiveness and sense of depth. Additionally, this framework also facilitates the scrutiny and inference of internal fracture processes. These results demonstrate that under specific service conditions, material deformation fracture probably stems from a combination of surface cracking and internal cracking rather than exclusively one or the other. Overall, our description and analysis of internally initiated cracking due to defects within the specimens can be beneficial in guiding future alloy design and optimization efforts.