The effect of La addition on primary α-Al and the eutectic Si phase of Al-7Si alloy is investigated systematically in this work. The results indicate that La addition causes a multi-refining efficiency on the microstructure of Al-7Si alloy, inc...
In this paper, we focus on the problem of contextual aggregation in the semantic segmentation of aerial images. Current contextual aggregation methods only aggregate contextual information within specific regions to improve feature representation, wh...
Protein structure refinement is a crucial step for more accurate protein structure predictions. Most existing approaches treat it as an energy minimization problem to intuitively improve the quality of initial models by searching for structures with...
Multi-agent trajectory prediction is essential for autonomous driving systems, as its performance heavily depends on the quality of feature representations. This paper proposes PFR-HiVT, a lightweight and effective approach for multi-agent trajectory...
With the rapid development of intelligent transportation, there comes huge demands for high-precision road network maps. However, due to the complex road spectral performance, it is very challenging to extract road networks with complete topologies....
Robust and accurate face alignment remains a challenging task, especially when local noises, illumination variations and partial occlusions exist in images. The existing local search and global match methods often misalign due to local optima without...
In this paper, the effects of a super-gravity field with multi-rotational speeds on the grain refinement and tensile properties of as-cast H13 steel were investigated systematically. The experimental results showed that compared to the single-rotatio...
Semantic segmentation is currently a hot topic in remote sensing image processing. There are extensive applications in land planning and surveying. Many current studies combine Convolutional Neural Networks (CNNs), which extract local information, wi...
Monocular Depth Estimation (MDE) remains a challenging problem due to texture ambiguity, occlusion, and scale variation in real-world scenes. While recent deep learning methods have made significant progress, maintaining structural consistency and ro...
Deep learning algorithms offer an effective solution to the inefficiencies and poor results of traditional methods for building a footprint extraction from high-resolution remote sensing imagery. However, the heterogeneous shapes and sizes of buildin...
Oriented object detection in remote sensing images rapidly evolves as a pivotal technique, driving transformative advancements across geospatial analytics, intelligent transportation systems, and urban infrastructure planning. However, the inherent c...
The detection and monitoring of changes in urban buildings, as a major place for human activities, have been considered profoundly in the field of remote sensing. In recent years, comparing with other traditional methods, the deep learning-based meth...
Algorithms such as RRT (Rapidly exploring random tree), A* and their variants have been widely used in the field of robot path planning. A lot of work has shown that these detectors are unable to carry out effective and stable results for moving obje...
Various deep learning applications benefit from multi-task learning with multiple regression and classification objectives by taking advantage of the similarities between individual tasks. This can result in improved learning efficiency and predictio...
Traditional 3D reconstruction models have consistently faced the challenge of balancing high recall of object edges with maintaining a high precision. In this paper, we introduce a post-processing method, the Multi-Head Attention Refiner (MA-R), desi...
The fractal dimension (FD) is a classical nonlinear dynamic index that can effectively reflect the dynamic transformation of a signal. However, FD can only reflect signal information of a single scale in the whole frequency band. To solve this proble...
With the development of Agriculture 4.0, the requirements for sustainable agriculture and precision agriculture continue to grow. As one of the three major staple foods globally, the quality and safety of rice affect human health as well as social de...
Ship-radiated noise is one of the important signal types under the complex ocean background, which can well reflect physical properties of ships. As one of the valid measures to characterize the complexity of ship-radiated noise, permutation entropy...
Radar target detection probability will decrease as the target echo signal-to-noise ratio (SNR) decreases, which has an adverse influence on the result of multi-target tracking. The performances of standard multi-target tracking algorithms degrade si...
Rolling bearing health status monitoring is essential for identifying early failures and avoiding un-planned downtime in industrial systems. To overcome the problems of existing entropy methods with multiple faults that are easily confounded at diffe...
In light of the ambitious “dual carbon” targets, the refined oil supply chain faces challenges in balancing economic viability with environmental sustainability. Traditional resource allocation methods predominantly prioritize cost minimi...
Due to the addition of the drag reducer in refined oil pipelines for increasing the pipeline throughput as well as reducing energy consumption, the classical method based on the Darcy-Weisbach Formula for precise pressure loss calculation presents a...
A rolling bearing early fault diagnosis method is proposed in this paper, which is derived from a refined composite multi-scale approximate entropy (RCMAE) and improved coyote optimization algorithm based probabilistic neural network (ICOA-PNN) algor...
Water shortage problems are increasing in many water-deficient areas. Most of the current research on multi-source combined water supplies depends on an overall generalization of regional water supply systems, which are seldom broken down into the de...
The goal of multi-view graph clustering (MVGC) for remote sensing data is to obtain a consistent partitioning by capturing complementary and consensus information across multiple views. However, numerous ambiguous background samples in multi-view rem...
In this paper, we propose a method to refine the depth maps obtained by Multi-View Stereo (MVS) through iterative optimization of the Neural Radiance Field (NeRF). MVS accurately estimates the depths on object surfaces, and NeRF accurately estimates...
Currently, samples are a critical driving force in the application of deep learning. However, the use of samples encounters problems, such as an inconsistent annotation quality, mismatches between images and labels, and a lack of fine-grained labels....
Cross-view image-to-image translation refers to generating what the scenery looks like from different views at the same location, which involves simultaneous semantic and appearance translation. Previous works mainly focused on semantic translation t...
Deep learning-based methods for building extraction from remote sensing images have been widely applied in fields such as land management and urban planning. However, extracting buildings from remote sensing images commonly faces challenges due to sp...
The calibration of a multi-camera system for volumetric measurements is a basic requirement of reliable 3D measurements and object tracking. In order to refine the precision of the mapping functions, a new, tomographic reconstruction-based approach i...
Deep learning-based ship-detection methods have recently achieved impressive results in the synthetic aperture radar (SAR) community. However, numerous challenging issues affecting ship detection, such as multi-scale characteristics of the ship, clut...
In this study, a multi-objective optimization procedure with embedded topology optimization was presented. The procedure simultaneously optimizes the spatial arrangement and topology of bodies in a multi-body system. The multi-objective algorithm det...
Accurate extraction of building vector contours from high-resolution remote sensing images is a fundamental task for urban mapping and geographic information systems. However, existing approaches often suffer from blurred boundaries and geometric dis...
Automatic sleep stage classification of multi-channel sleep signals can help clinicians efficiently evaluate an individual’s sleep quality and assist in diagnosing a possible sleep disorder. To obtain accurate sleep classification results, the...
Accurate and efficient detection of small and complex surface defects on hot-rolled steel plates remains a significant challenge in industrial quality assurance. Current deep learning detectors often exhibit limitations in detection accuracy and trai...
The large-scale numerical simulation of complex flows has been an important research area in scientific and engineering computing. The lattice Boltzmann method (LBM) as a mesoscopic method for solving flow field problems has become a relatively new r...
Change detection based on bi-temporal remote sensing images has made significant progress in recent years, aiming to identify the changed and unchanged pixels between a registered pair of images. However, most learning-based change detection methods...
The accurate prediction of the future trajectories of traffic participants is crucial for enhancing the safety and decision-making capabilities of autonomous vehicles. Modeling social interactions among agents and revealing the inherent relationships...
The increasing adoption of artificial intelligence (AI) in intelligent healthcare systems has elevated the demand for robust medical imaging and vision-based inventory solutions. For an intelligent healthcare inventory system, accurate recognition an...
Deep convolution neural network (DCNN) technology has achieved great success in extracting buildings from aerial images. However, the current mainstream algorithms are not satisfactory in feature extraction and classification of homesteads, especiall...
SAR target detection and signal detection are critical tasks in electromagnetic signal processing, with wide-ranging applications in remote sensing and communication monitoring. However, these tasks are challenged by complex backgrounds, multi-scale...
Detecting dense text in scene images is a challenging task due to the high variability, complexity, and overlapping of text areas. To adequately distinguish text instances with high density in scenes, we propose an efficient approach called DenseText...
Co-deformation of Al and Ti by accumulative roll bonding (ARB) with intermediate heat treatments is utilized to prepare multi-layered Ti/Al sheets. These sheets show a high specific strength due to the activation of various hardening mechanisms impos...
Semi-supervised joint classification of Hyperspectral Images (HSIs) and LiDAR-derived Digital Surface Models (DSMs) remains challenging due to scarcity of labeled pixels, strong intra-class variability, and the heterogeneous nature of spectral and el...
Extracting buildings in urban scenes from remote sensing images is crucial for the construction of digital cities, urban monitoring, urban planning, and autonomous driving. Traditional methods generally rely on shadow detection or stereo matching fro...
We present a multi-sensor data fusion model based on a reconfigurable module (RM) with three fusion layers. In the data layer, raw data are refined with respect to the sensor characteristics and then converted into logical values. In the feature laye...
Complex single/multi-body structures are generally found in ship and ocean engineering. They have the smooth, sharp, concave, and convex surface features in common. Precise modeling of the structures is the basis of numerical simulation. However, the...
Shape-from-shading and stereo vision are two complementary methods to reconstruct 3D surface from images. Stereo vision can reconstruct the overall shape well but is vulnerable in texture-less and non-Lambertian areas where shape-from-shading can rec...
Semantic modeling is a challenging task that has received widespread attention in recent years. With the help of mini Unmanned Aerial Vehicles (UAVs), multi-view high-resolution aerial images of large-scale scenes can be conveniently collected. In th...
Although remarkable progress has been made in salient object detection (SOD) in natural scene images (NSI), the SOD of optical remote sensing images (RSI) still faces significant challenges due to various spatial resolutions, cluttered backgrounds, a...
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