The effectiveness of deep learning models depends on their architecture and topology. Thus, it is essential to determine the optimal depth of the network. In this paper, we propose a novel approach to learn the optimal depth of a stacked AutoEncoder,...
In current decades, significant advancements in robotics engineering and autonomous vehicles have improved the requirement for precise depth measurements. Depth estimation (DE) is a traditional task in computer vision that can be appropriately predic...
This paper presents a depth upsampling method that produces a high-fidelity dense depth map using a high-resolution RGB image and LiDAR sensor data. Our proposed method explicitly handles depth outliers and computes a depth upsampling with confidence...
A lot of research has been conducted in recent years on stereo depth estimation techniques, taking the traditional approach to a new level such that it is in an appreciably good form for competing in the depth estimation market with other methods, de...
In the heat treatment of steel, decarburization is a serious issue that leads to poor wear resistance and low fatigue life. At present, the decarburization depth was determined using a visual estimation by the human eye, and the software estimation w...
Snow depth is a critical parameter for characterizing snow dynamics and water resources, and its accurate inversion is essential for hydrological processes, climate studies, and disaster prevention in cold regions. Based on long-term daily ground met...
This paper investigates the impact of adaptive network depth selection on the robustness and performance of Regenerative Reinforcement Learning (RRL) models. RRL accelerates learning by reusing previously computed results. We propose a task-driven ap...
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 fa...
The study of penetration depth of gravity piston samplers has an essential impact on sampling efficiency and instrument safety. This study focuses on predicting penetration depth based on the characteristic parameters of the sampled seafloor sediment...
In this study, a machine learning algorithm was introduced to fuse gridded snow depth datasets. The input variables of the machine learning method included geolocation (latitude and longitude), topographic data (elevation), gridded snow depth dataset...
The purpose of this study is to investigate the effect of the seating condition on learning tasks. This physical setting and these cognitive activities (that participants then test) are not the totality of education practice and context, but desks an...
Accurately sensing the surrounding 3D scene is indispensable for drones or robots to execute path planning and navigation. In this paper, a novel monocular depth estimation method was proposed that primarily utilizes a lighter-weight Convolutional Ne...
Depth estimation can provide tremendous help for object detection, localization, path planning, etc. However, the existing methods based on deep learning have high requirements on computing power and often cannot be directly applied to autonomous mov...
Urban flood control requires real-time and spatially detailed information regarding the waterlogging depth over large areas, but such information cannot be effectively obtained by the existing methods. Video supervision equipment, which is readily av...
The use of high-throughput phenotyping with imaging and machine learning to monitor seedling growth is a tough yet intriguing subject in plant research. This has been recently addressed with low-cost RGB imaging sensors and deep learning during day t...
Water volume, a fundamental characteristic of lakes, serves as a crucial indicator for understanding regional climate, ecological systems, and hydrological processes. However, limitations in existing estimation methods and datasets for water depth, s...
Predicting the frost depth of soils in pavement design is critical to the sustainability of the pavement because of its mechanical vulnerability to frozen-thawed soil. The reliable prediction of frost depth can be challenging due to the high uncertai...
Recent studies on self-supervised monocular depth estimation have achieved promising results, which are mainly based on the joint optimization of depth and pose estimation via high-level photometric loss. However, how to learn the latent and benefici...
Burn injuries are a common traumatic condition, and the early diagnosis of burn depth is crucial for reducing treatment costs and improving survival rates. In recent years, image-based deep learning techniques have been utilized to realize the automa...
Monocular depth prediction research is essential for expanding meaning from 2D to 3D. Recent studies have focused on the application of a newly proposed encoder; however, the development within the self-supervised learning framework remains unexplore...
Classification of indoor environments is a challenging problem. The availability of low-cost depth sensors has opened up a new research area of using depth information in addition to color image (RGB) data for scene understanding. Transfer learning o...
This paper proposes a novel unsupervised learning framework for depth recovery and camera ego-motion estimation from monocular video. The framework exploits the optical flow (OF) property to jointly train the depth and the ego-motion models. Unlike t...
Advances in visual odometry (VO) systems have benefited from the widespread use of affordable RGB-D cameras, improving indoor localization and mapping accuracy. However, older sensors like the Kinect v1 face challenges due to depth inaccuracies and i...
Grading the quality of fresh cut flowers is an important practice in the flower industry. Based on the flower maturing status, a classification method based on deep learning and depth information was proposed for the grading of flower quality. Firstl...
Penetration depth is a critical factor determining joint strength in butt welding; however, it is difficult to monitor in keyhole-mode laser welding due to the dynamic nature of the keyhole. Recently, optical coherence tomography (OCT) has been intro...
As a key component of remote-sensing technology, satellite gravity observation offers extensive coverage and high accuracy, effectively compensating for the shortcomings of terrestrial gravity measurements. Three-dimensional gravity data inversion ca...
Depth completion generates a comprehensive depth map by utilizing sparse depth data inputs, supplemented by guidance provided by an RGB image. Deep neural network models depend on annotated datasets for optimal training. However, when the quantity of...
Thermal cameras are known for their ability to overcome lighting constraints and provide reliable thermal radiation images. This capability facilitates methods for depth and ego-motion estimation, enabling efficient learning of poses and scene struct...
Herein, we propose an unsupervised learning architecture under coupled consistency conditions to estimate the depth, ego-motion, and optical flow. Previously invented learning techniques in computer vision adopted a large amount of the ground truth d...
We propose a completely unsupervised approach to simultaneously estimate scene depth, ego-pose, ground segmentation and ground normal vector from only monocular RGB video sequences. In our approach, estimation for different scene structures can mutua...
Background: Electroencephalography (EEG) signals are widely used for monitoring anesthesia depth during surgery. Current commercial indicators are largely closed-source and may reflect dynamic changes with some delay. Methods: This study proposes a m...
The term “anesthetic depth” refers to the extent to which a general anesthetic agent sedates the central nervous system with specific strength concentration at which it is delivered. The depth level of anesthesia plays a crucial role in d...
Self-supervised monocular depth estimation has demonstrated high practical utility, as it can be trained using a photometric image reconstruction loss between the original image and a reprojected image generated from the estimated depth and relative...
Machine learning-based computer vision techniques using depth cameras have shown potential in physiotherapy movement assessment. However, a comprehensive understanding of their implementation, effectiveness, and limitations remains needed. Following...
Blowing snow is a common phenomenon over the Antarctic ice sheet and sea ice regions, playing a crucial role in the Antarctic climate system. Previous research developed an optimized machine learning (ML) model to diagnose blowing snow occurrence usi...
Computing the temporal variation in clearwater scour depth around abutments is important for bridge foundation design. To reach the equilibrium scour depth at bridge abutments takes a very long time. However, the corresponding times under prototype c...
Accurate high spatial resolution snow depth mapping in arid and semi-arid regions is of great importance for snow disaster assessment and hydrological modeling. However, due to the complex topography and low spatial-resolution microwave remote-sensin...
Deep learning is the mainstream paradigm in computer vision and machine learning, but performance is usually not as good as expected when used for applications in robot vision. The problem is that robot sensing is inherently active, and often, releva...
Accurately predicting the carbonation depth of recycled aggregate (RA) concrete is essential for durability assessment. Based on a dataset of 682 experimental samples, this study employed seven machine learning algorithms to develop prediction models...
Recent advances in optics and computing technologies have encouraged many applications to adopt the use of three-dimensional (3D) data for the measurement and visualization of the world around us. Modern 3D-range scanning systems have become much fas...
We present an occlusion-aware unsupervised neural network for jointly learning three low-level vision tasks from monocular videos: depth, optical flow, and camera motion. The system consists of three different predicting sub-networks simultaneously c...
Hyperspectral remote sensing images offer a unique opportunity to quickly monitor water depth, but how to utilize the enriched spectral information and improve its spatial resolution remains a challenge. We proposed a water depth estimation framework...
Aerosol optical depth (AOD) constitutes a key parameter of aerosols, providing vital information for quantifying the aerosol burden and air quality at global and regional levels. This study demonstrates a machine learning strategy for retrieving AOD...
In large-scale laying hen farming, timely detection of dead chickens helps prevent cross-infection, disease transmission, and economic loss. Dead chicken detection is still performed manually and is one of the major labor costs on commercial farms. T...
Underwater Wireless Sensor Networks (UWSNs) have aroused increasing interest of many researchers in industry, military, commerce and academe recently. Due to the harsh underwater environment, energy efficiency is a significant theme should be conside...
Seasonal freezing depth change is important in many environmental science and engineering applications. However, such changes are rare at region scales, especially over China, in the long time series. In this study, we evaluated the annual changes in...
This study examines how generative AI (GAI) impacts primary students’ in-depth learning, focusing on critical thinking and prior knowledge. A quasi-experiment involved 163 sixth-graders divided into three groups: a control group (lecture-based...
Snow depth estimation with passive microwave (PM) remote sensing is challenged by spatial variations in the Earth’s surface, e.g., snow metamorphism, land cover types, and topography. Thus, traditional static snow depth retrieval algorithms can...
Current remote sensing-based aerosol optical depth (AOD) products have coarse spatial resolutions, which are useful for studies at continental and global scales, but unsatisfactory for local scale applications, such as urban air pollution monitoring....
Due to the increase in aging structures and the decrease in construction workforce, there is an increasing interest in automating structural damage monitoring. Surface damage on concrete structures, such as cracks, delamination, and rebar exposure, i...
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